
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01773-5
10.1016/j.isci.2024.110548
110548
Article
Preserved functional organization of auditory cortex in two individuals missing one temporal lobe from infancy
Regev Tamar I. tamarr@mit.edu
12910∗
Lipkin Benjamin lipkinb@mit.edu
129∗∗
Boebinger Dana 34
Paunov Alexander 7
Kean Hope 12
Norman-Haignere Sam V. 3456
Fedorenko Evelina evelina9@mit.edu
128∗∗∗
1 Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
2 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
3 Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
4 Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
5 Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
6 Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
7 INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, Gif sur Yvette, France
8 Speech and Hearing Bioscience and Technology (SHBT) Program, Harvard University, Boston, MA, USA
∗ Corresponding author tamarr@mit.edu
∗∗ Corresponding author lipkinb@mit.edu
∗∗∗ Corresponding author evelina9@mit.edu
9 These authors contributed equally

10 Lead contact

22 7 2024
20 9 2024
22 7 2024
27 9 11054818 8 2023
19 1 2024
16 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Human cortical responses to natural sounds, measured with fMRI, can be approximated as the weighted sum of a small number of canonical response patterns (components), each having interpretable functional and anatomical properties. Here, we asked whether this organization is preserved in cases where only one temporal lobe is available due to early brain damage by investigating a unique family: one sibling missing their left temporal lobe from infancy, another missing the right temporal lobe from infancy, and a third anatomically neurotypical. None of the siblings manifested behavioral deficits. We analyzed fMRI responses to diverse natural sounds within the intact hemispheres of these individuals and compared them to 12 neurotypical participants. All siblings manifested typical-like auditory responses in their intact hemispheres. These results suggest that the development of the auditory cortex in each hemisphere does not depend on the existence of the other hemisphere, highlighting the redundancy and equipotentiality of the bilateral auditory system.

Graphical abstract

Highlights

• Auditory cortex (AC) organization tested in participants with one temporal lobe

• fMRI voxel decomposition of AC responses to natural sounds was used

• Features of auditory components, including speech and music, appear typical-like

• AC organization is robust to whether it is bilateral or present in one hemisphere

Neuroscience; Clinical neuroscience; Cognitive neuroscience.

Subject areas

neuroscience
clinical neuroscience
cognitive neuroscience
Published: July 22, 2024
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pmcIntroduction

Mature neural tissue in human brains stores a lifetime of experiences, knowledge, and skills. As a result, brain damage sustained in adulthood often causes a loss of perceptual, motor, or cognitive functions. In contrast, the outcomes of brain damage sustained early in life are more variable. For example, perinatal strokes (strokes in fetuses or newborns), which occur in approximately 1 in 2,000 term births,1 can lead to severe long-term disabilities,2,3 but may also show no observable effects and even go undetected for many years.4,5

The factors that determine the severity of perinatal lesion outcomes as well as the associated brain reorganization are not completely understood. The location of the injury appears to be important,3 presumably because brain areas vary in how replaceable they are, i.e., to what extent their functions could be performed by other brain areas in case they are damaged. How replaceable a brain area is, likely depends, at least in part, on its maturation trajectory. Subcortical and brainstem structures develop earlier than cortical structures.6 In the cortex, primary sensory and motor areas develop earlier than the association cortical areas,6 some of which continue to mature well into late childhood and adolescence.7,8 Indeed, one relatively late-developing function—language—exhibits a striking contrast between late damage to the left hemisphere (LH), which typically leads to linguistic deficits (aphasia) and early damage (within the first few years of life) to the LH, which often results in normally developing linguistic functions, supported by the right hemisphere (RH).5,9,10,11,12,13 However, even in cases of damage to primary cortical areas, recovery of brain function has been reported (see studies by Knyazeva at al.14 and Kiper et al.15 for evidence of visual function recovery following extensive early damage to primary visual cortex). As such, many questions remain about the relationship between early brain damage and its long-term effects on brain function and cortical organization.

Our investigation concerns the organization of auditory cortex following extensive early unilateral lesions of the temporal lobe. Unilateral damage to temporal lobe structures may lead to severe auditory impairments when it occurs in adults16 and children17 even as early as 13 months of age.18 However, to our knowledge, the effects of perinatal temporal lobe lesions on auditory cortical organization have not been extensively investigated. Does the auditory cortex in the intact hemisphere look typical-like, or does the lack of the contralateral homotopic areas alter its functional architecture in some way? Answers to these questions can inform our understanding of the constraints on brain development and on the possible architectures for perceptual and cognitive functions.

We investigated the functional organization of auditory cortex in three members of an unusual family. Two siblings in this family (POI 1 and 2; ages 54 and 55 at testing) had extensive unilateral lesions that encompassed large parts of the temporal lobe, likely due to perinatal stroke: in one sibling (POI 1), the left temporal lobe was affected (Figure 1A), and in the other (POI 2), the right temporal lobe was affected (Figure 1B, see Figure S1 for a more detailed characterization of the anatomy of the lesions). A third sibling (POI 3; age 53 at testing) had an intact brain (Figure 1C) and therefore served as a control. Importantly, the affected individuals reported normal auditory, linguistic, and general cognitive abilities, as was confirmed by our behavioral testing (STAR Methods, results). One of the affected individuals, described in studies by Tuckute et al.5 and Li et al.,19 was not even aware of their lesion until approximately the age of 25.Figure 1 Anatomical MRI images for the three participants of interest (POI)

(A) POI 1 is missing most of the left temporal lobe from infancy; (B) POI 2 is missing most of the right temporal lobe from infancy; and (C) POI 3 has a typical brain. See Figure S1 for a more detailed characterization of lesion anatomy.

What might we expect regarding the functional architecture of the auditory cortex in the absence of the contralateral temporal lobe, given no report of impaired behavior? One possibility is an overall increase in auditory representation to compensate for the loss of contralateral tissue. Following brain injury, neural tissue that is able to take over the lost functions often expands.20,21 This increased representation could apply to all auditory functions (Figure 2, hypothesis 1), or it could be restricted to—or especially pronounced for—auditory areas that support specific functions (Figure 2, hypothesis 2). For the latter, auditory functions that were suggested to be lateralized in neurotypical brains are of special interest.Figure 2 Hypotheses for functional organization of the auditory cortex in the presence of extensive contralateral temporal lobe lesions

The colored circles represent neural activation to functional auditory component (each color corresponds to a different component).

(A) Activations observed in neurotypicals.

(B) Hypotheses for activations observe in individuals missing one temporal lobe from birth.

Whether different aspects of audition preferentially depend on one or the other hemisphere is a question that has garnered much attention over the years22,23,24 and remains debated. According to one prominent proposal, the left auditory cortex is better suited for processing fast-changing auditory information,25,26,27,28,29,30,31 which may be important for processing the phonetic content of speech (cf. McGettigan and Scott32 for arguments against this claim). The right auditory cortex, on the other hand, is postulated to be better suited for processing fine spectral modulations over longer time scales,31,33,34,35 which may be important for processing prosodic features of speech (intonation) or pitch information more broadly, including in music.22,36,37,38 If the left auditory cortex is indeed more suitable for speech perception, then we might expect speech processing to take up more cortical tissue and/or elicit stronger responses in the RH in the absence of the left temporal lobe compared to neurotypicals, because the RH auditory cortex is just not as well designed for this function. And similarly, if the right auditory cortex is more suitable for music perception, then we might expect music perception to take up more cortical tissue and/or elicit stronger responses in the LH in the absence of the right temporal lobe. Alternatively, if auditory functions are redundantly supported by the two hemispheres, then we might expect the auditory organization in the intact hemisphere to look typical-like (Figure 2, hypothesis 3).

To investigate the organization of the auditory cortex in our participants of interest (POI), we adopted a paradigm developed by Norman-Haignere et al.39 Norman-Haignere and colleagues recorded functional magnetic resonance imaging (fMRI) responses to a diverse set of natural sounds and, using a data-driven approach, uncovered six response components that capture most of the explainable variance in auditory cortical responses (see study by Boebinger et al.40 for replication). Components 1 and 2 were selective for low- and high-frequency acoustic information, respectively, as confirmed by a separate assessment of tonotopy39; components 3 and 4 were selective for spectrotemporal modulations that tend to be present in environmental sounds and pitched sounds, respectively; and components 5 and 6 were highly selective for speech and music, respectively (Figure 3D). We tested whether this auditory cortical organization is preserved when the auditory cortex in the contralateral hemisphere is missing from infancy.Figure 3 Experiment design and auditory component estimation procedure

Adapted from studies by Norman-Haignere et al.39 and Boebinger et al.40 with permission.

(A) The 30 sound stimuli presented in the experiment. This set of 30 stimuli was chosen from the original set of 165 natural sounds in order to optimize the detection of the six components40 (STAR Methods). The 30 sounds are ordered here by a score of how well they were suited for optimizing the detection of the six components.

(B) Experimental paradigm. Each 2-s sound stimulus was repeated three times consecutively, with one repetition (the second or third) being 8 dB quieter. Participants were instructed to press a button when they detected this quieter sound. One fMRI volume was acquired in the silent period between stimuli (sparse scanning).

(C) Procedure of component voxel weight estimation. Whereas Norman-Haignere et al.39 and Boebinger et al.40 estimated both a component response profile matrix and a weight matrix, we used the component response matrix from Norman-Haignere et al.39 and estimated just the voxel weight matrix given our data matrix, using a general linear model (GLM, STAR Methods).

(D) Component responses, averaged across sounds from the same category, reveal that Component 5 is selective to speech and Component 6 is selective to music. (Sounds were assigned to categories by an independent set of participants in an online study, as described in Norman-Haignere et al.39) Error bars represent one standard error of the mean across sounds from a category, computed using bootstrapping (10,000 samples).

Results

Behavioral results

POI 1

POI 1 was missing large parts of their left temporal lobe from infancy. In line with POI 1’s self-report, they performed within normal range on all language and general cognitive tasks. They got 90% correct on Peabody Picture Vocabulary Test (PPVT), 99% correct on Test for Reception of Grammar (TROG), and 97.6, 98.6, and 98.4 on the aphasia, language, and cortical quotients of the Western Aphasia Battery - Revised (WAB-R) (the criterion cut-off score for diagnosis of aphasia is an aphasia quotient of 93.8). POI 1’s performance was therefore not distinguishable from the performance of neurotypical controls. Their Kaufman Brief Intelligence Test (KBIT) scores were 130 (98th percentile) on the verbal composite assessment (across the two subtasks), 54 (79th percentile) on the non-verbal assessment, and 122 (93rd percentile) overall composite assessment. They answered 51 of the 52 questions correct on the Pyramids and Palm Trees task.

POI 2

POI 2 was missing large parts of their right temporal lobe from infancy. POI 2 performed within normal range on all language and general cognitive tasks. They got 83.3% correct on PPVT, 96.25% correct on TROG, and 100 and 100 on the aphasia, and language quotients (cortical quotients was not completed) of the WAB-R (the criterion cut-off score for diagnosis of aphasia is an aphasia quotient of 93.8). POI 2’s performance was therefore not distinguishable from the performance of neurotypical controls. Their KBIT scores were 115 (84th percentile) on the verbal composite assessment (across the two subtasks), 109 (73rd percentile) on the non-verbal assessment, and 117 (79th percentile) overall composite assessment. They answered 50 of the 52 questions correct on the Pyramids and Palm Trees task.

POI 3

POI 3 had a typical-like brain. POI 3 performed within normal range on all language and general cognitive tasks that were administered to them. They got 87.5% correct on PPVT, 95% correct on TROG. WAB-R was not administered. POI 3’s performance was therefore not distinguishable from the performance of neurotypical controls. Their KBIT scores were 145 (99.9th percentile) on the verbal composite assessment (across the two subtasks), 125 (95th percentile) on the non-verbal assessment, and 139 (99.5th percentile) overall composite assessment. Pyramids and Palm Trees task was not administered.

fMRI results

We measured fMRI responses to a set of 30 natural sounds from a variety of categories (Figures 3A and 3B) in three participants of interest (POIs, Figure 1): three siblings, with one missing most of their left temporal lobe (POI 1, see Figure S1 for a detailed characterization of lesion anatomy), one missing most of their right temporal lobe (POI 2), and the third neurotypical with both temporal lobes intact (POI 3), as well as in a control population of 12 neurotypical participants. The 30 sounds presented in this experiment were a subset of the 165 sounds originally tested in a study by Norman-Haignere et al.39 and were selected to optimally identify component weights40 (STAR Methods).

We then projected the response of each voxel to the set of 30 sounds (data matrix, Figure 3C) onto the six auditory response components from the study by Norman-Haignere et al.39 (response profile matrix, Figure 3C). The projection was done using a general linear model (GLM) with ordinary least-squares regression providing estimated weights for each of the six components for each voxel and participant (weight matrix, Figure 3C). This projection allowed us to decompose cortical auditory responses into functionally meaningful components (Figure 3D). We studied the component responses (weights) within the auditory cortex, which was defined as the conjunction of 15 anatomical parcels tiling the anatomical space of sound-responsive cortex41 (selection of parcels as in Boebinger et al.40 STAR Methods; Figure 4).Figure 4 Functional auditory cortex components in intact hemispheres of the three participants of interest, relative to probability maps derived from the 12 control participants

For each component (panels A–F; the 6 auditory components functionally corresponding to (A) low- and (B) high- frequency, (C) environmental, (D) pitched, (E) speech, and (F) music sounds; although note that the functional correspondence of components 3 and 4 is less well established) and hemisphere, probabilistic maps (blue to yellow color scale) depict for each voxel the percentage of control participants who show a significant response (p < 0.05). Voxels for which no control participant showed a significant response were left gray, as were voxels outside of the union of the 15 parcels chosen to comprise auditory cortex (depicted by the white outline, selection of parcels41 as in the study by Boebinger et al.40; STAR Methods). On top of these maps, the largest contiguous cluster of voxels passing the same threshold is outlined in orange for POI 1, pink for POI 2, and black for POI 3. Asterisks indicate a significant (p < 0.05) Pearson correlation across the 15 anatomical parcels between the spatial pattern of each POI’s component response and the average component response of the 12 neurotypical participants (for full statistical details see Table S5). See all maps at osf.io/qrx5n/.

In order to compare the explanatory power of these components in the POIs to that of the neurotypical controls, we calculated the proportion of the variance explained by the six components. For each voxel in the auditory cortex of each participant, we estimated the amount of variance that was explained by the six components relative to the total variance in that voxel: 1-Var(ŷ-y)/Var(y) where ŷ is the estimate of the signal using all six components and y is the observed response (y = ŷ + residual of the model). For the 12 neurotypical participants, the mean variance-explained (across all included auditory voxels across the two hemispheres) ranged from 30.7% to 40.3% (note that these values are expected to be well below 100% because the recorded blood-oxygen-level-dependent (BOLD) response reflects more than purely neurally explainable phenomena). The mean variance-explained in each of the three POIs fell right in the middle of this range (POI 1: 36.5%, POI 2: 34.2%, and POI 3: 37.8%), which suggests that the POIs are comparable to the controls in terms of how well their auditory neural activity is captured by the six components. Of note, the noise ceiling of the data was similar between the POIs and the controls, as estimated using split-half correlations across the whole auditory cortex as well as for each component separately (STAR Methods section “data reliability” and Figure S2).

All subsequent analyses were performed component-wise within the auditory cortex of the intact hemisphere in the POIs, and the results were compared to the same hemisphere in the neurotypical controls. To compare the organization of auditory cortex in the POIs to that of neurotypical controls, we examined three functional characteristics of the observed responses: (1) the response magnitude and spatial extent of each component—both of which served for testing the hypotheses of increased representations compensating for the missing contralateral temporal lobe (Figure 2); (2) the spatial layout of the components; and (3) the stability of the component topographies across scanning runs. The latter two measures were used to test a more general hypothesis about potential differences between the POIs and the controls.

Response magnitude and spatial extent

For each of the three POIs and each of the 12 control participants, the mean response magnitude (weights) and the spatial extent (number of significant voxels at the p < 0.05 level within the auditory mask) were calculated for each component (Figures 5A and 5B). These measures served as proxies for testing the hypotheses that all (hypothesis 1, Figure 2) or some (hypothesis 2, Figure 2) of the functional auditory components show increased representations in the POIs, to compensate for the missing contralateral temporal lobes. We expected to measure relatively high mean response magnitudes and/or spatial extents for any functional component that manifests increased representation in the POI relative to the controls.Figure 5 Comparison—for the different properties of the six components—between the three participants of interest and the 12 control participants

Each violin plot represents the 12 control participants. Within the violin plots, white dots represent the median and a gray horizontal line represents the mean. Vertical thin boxes represent the upper and lower quartiles, and the violins are plotted between the rang of the data points. For each measure (A–D) and component (labels on the lowest x axis), the two violins represent the left hemisphere (left) and right hemisphere (right). The first two measures—response magnitude (A) and spatial extent (B) are proxies for the amount of neural activity and were both used to address the hypotheses regarding increased representations compensating for the missed hemisphere (Figure 2, introduction). The second two measures—similarity of spatial layout between a participant and the population (C) and stability of topographies across scanning runs (D) reflect information about the robustness of the fine-grained activation patterns. See results and STAR Methods for more details. Horizontal lines represent POI 1 (orange) missing most of the left temporal lobe from infancy, POI 2 (magenta) missing most of the right temporal lobe from infancy, and POI 3 (black) with a typical brain. Asterisks indicate significance according to a log likelihood ratio test (p < 0.01, for full statistical details, as well as levels of evidence in favor of (or against) the null hypothesis as per a relative likelihood factor computed using AICc values, see Table S4 for full statistical results, STAR Methods). The color of the stars matches the POI color.

For statistical testing, we contrasted two linear mixed-effects (LME) models (for each component, hemisphere, and measure separately); the first model (M1) assuming that all the neurotypical control participants and the POI belong to the same Gaussian distribution, and the second model (M2) assuming that the POI belongs to a distinct distribution compared to the controls (STAR Methods). We used two ways to compare the models: (1) using a likelihood ratio test (STAR Methods) and (2) directly comparing their corrected Akaike Information Criterion (AICc; Cavanaugh42); the two methods gave almost identical results. Importantly, using the AICc scores for M1 and M2, allowed us to calculate the relative likelihood of M1 with respect to M2 (STAR Methods; a study by Burnham and Anderson43). This relative likelihood value can be interpreted as how much more probable M1 is than M2 given the data, which is similar to a Bayes factor, but more computationally efficient and explicitly accounts for model complexity and the small sample size.

For 21 of the 24 measures examined (6 components × 2 POIs × 2 measures; response magnitude and spatial extent), the statistical analyses indicated that the POIs and the control participants belong to the same distribution (M1); for 18 of those measures we find moderate evidence for M1 (relative likelihood between 3 and 10, following a study by Kass and Raftery;44 Table S4, STAR Methods) and for the remaining 3 measures—mild evidence for M1 (relative likelihood between 1 and 3). Only for 3 of the 24 measures did the statistical analyses indicate that the POIs differ significantly from the controls, although for all 3 measures, we find only mild evidence for M2 (relative likelihood between 0.33 and 1, Table S4, STAR Methods). One of these measures was the mean response magnitude of component 3 of POI 2 (missing RH temporal lobe), which was lower than that of the control participants (Figures 5A and 5B; Table S1) and therefore did not confirm hypotheses 1 or 2 (Figure 2). It is important to note that component 3, which was associated in past studies to the processing of environmental sounds,39 was shown to be less reliable compared to the other components both between (Figure 2A in the study by Boebinger et al.40) and within (Figure S5 in the study by Norman-Haignere et al.39) neurotypical participants. Thus, the low response magnitude of Component 3 in POI 2 may be explained by its overall low reliability in neurotypical individuals rather than a non-typical presentation in POI 2. The other two measures were the mean response magnitude and spatial extent of component 2 of POI 1 (missing LH temporal lobe), which were significantly larger than those of the control participants (Figures 5A and 5B; Table S4). Component 2 was previously associated to the processing of high frequencies in primary auditory cortex, as evidenced by its anatomical co-localization with the areas that respond to high frequencies in independent tonotopy assessment for the same individuals.39 This result (increased representation of Component 2) was not predicted based on the prior literature and will be further discussed in the following.

Spatial layout

We examined the anatomical spatial distribution of response components in two ways. First, we plotted each component for each POI on top of a probabilistic map that we computed for the 12 control participants using the binarized significant (p < 0.05, uncorrected) voxel responses. This approach allowed for a visual assessment of each component in the POIs (binarized in a similar way) relative to the control population. The POIs’ components fell qualitatively within the boundaries of the neurotypical component distributions (Figure 4). The only exception was Component 3 in the (left) auditory cortex of POI 2, which was almost absent (Figure 4C). However, note that the location of component 3 was more variable than the other components across the 12 neurotypical control participants (Figure 4C, values of the probabilistic maps are lower for Component 3 versus the other components), which is in line with previous reports of lower reliability of Component 3 compared to other components, as noted above.

Second, we quantified the similarity of the overall spatial component layout in the POIs vs. controls via correlations across 15 anatomical parcels chosen to comprise auditory cortex41 (selection of parcels as in the study by Boebinger et al.40; STAR Methods). We chose to project the responses to this lower-dimensional space because establishing voxel-wise functional correspondences across individuals is challenging due to inter-individual variability in the auditory cortex.45,46 In particular, for each component, a Pearson correlation was computed across the 15 parcels between each POI’s component response and the average component response of the controls. POI 1 presented with a significant correlation to the control group for four of the six components (components 1, 2, 5, and 6, p < 0.01 false discovery rate [FDR] corrected, Figure 4; Table S5), POI 2—for three of the six components (components 1, 2, and 6), and POI 3—for five of the six components for each of the two hemispheres (components 1, 2, 4, 5, and 6). Component 3 was not significantly correlated with the control group in any of the POIs, in line with its lower reliability, as noted previously.

To further investigate the extent to which the observed correlation coefficients were within the expected range of individual differences, we performed the same correlation analysis reported above for each of the 12 control participants via their correlation with the average of the remaining 11 control participants. We then tested whether the values of the correlation coefficients obtained for the POIs (comparing the POIs to the average of the 12 controls) differed significantly from the values of the correlation coefficients obtained for the control group (comparing each control participant to the average of the remaining 11 controls). For statistical testing we used LME comparison as above (STAR Methods). The only case (out of 12: 6 components × 2 POIs) where a significant difference was observed between (1) the correlation coefficients obtained when comparing each of the POIs to the control group and (2) the correlation coefficients obtained when comparing each of the 12 control participants to the average of the remaining 11 controls was for Component 3 in POI 2 (Figure 5C; Table S4). The remaining cases reported above where the correlation of spatial layout between POIs and the control population did not reach significance likely reflected spatial variability across the neurotypical population rather than a non-typical layout of the POIs’ components.

Stability of the component topographies across scanning runs

To examine the stability of the component topographies over time, we correlated the response profiles—across the same 15 parcels41 that were used in the analysis of the spatial layout—between odd- and even-numbered scanning runs. POI 1 showed significantly stable patterns of activation for three of the six components (components 1, 2, and 5), POI 2—for four of the six components (components 1, 2, 4, and 5), and POI 3 for five of the six (components 1, 2, 3, 4, and 5) LH components and for all RH components (Table S5). As in the previous section, this process was repeated for each of the control participants to establish a null distribution of between-run correlations and test whether the less stable component topographies observed in the POIs were also observed in neurotypicals or rather were unique to the POIs. The only significant difference (out of 12: 6 components × 2 POIs) between (1) the correlation coefficients obtained when comparing component spatial patterns between odd and even runs in a POI and (2) the correlation coefficients obtained when comparing component spatial patterns between odd and even runs in each of the 12 control participants was for component 4 in POI 1 (Figure 5D; Table S4). The remaining cases reported above where the stability of component topographies across time was not significant likely reflected temporal instability present in the neurotypical population rather than non-typical instability in the POIs.

Discussion

The current study examined a rare case of two siblings who are each lacking large parts of one of their temporal lobes from infancy: one in the left hemisphere, the other in the right. We investigated whether the organization of the intact auditory cortex in these individuals is similar to that in typical brains. To do this, we used fMRI to measure neural responses to natural sounds in these two siblings, a third (neurotypical) sibling, and 12 additional neurotypical control participants. We decomposed those responses into six functional components39,40 and investigated the properties of the components in the siblings and the controls. Our findings suggest that the functional organization of the auditory cortex in the intact hemispheres is preserved in the affected siblings despite lacking the other temporal lobe from infancy.

The siblings that lacked one temporal lobe (participants of interest, POIs) manifested intact auditory, linguistic, and general cognitive abilities. However, how the auditory cortex in their intact hemispheres gives rise to this normative performance has not been investigated. In fact, we are not aware of any previous attempts to investigate the functional architecture of auditory areas in cases of highly atypical temporal-lobe anatomy using modern brain imaging approaches. One possibility is that auditory representations in the intact hemisphere would be increased to compensate for the loss of the contralateral temporal lobe. This possibility seems likely if both hemispheres are essential for proper auditory processing in typical brains. Alternatively, one auditory cortex may be sufficient for proper auditory function. We tested whether all (hypothesis 1, Figure 2), some (hypothesis 2), or none (hypothesis 3) of the auditory components would show increased representation in the siblings missing their temporal lobes.

Increased representation can manifest as higher response magnitude and/or greater spatial extent of activation. Accordingly, we focused on these two brain measures in evaluating the three hypotheses. Hypothesis 1 was clearly refuted: it was not the case that all of the components showed increased representation in the affected siblings. Hypothesis 2 (that some components show increased representation) was then compared to hypothesis 3 (that none of the components show increased representation) for each component and POI separately. The only component for which hypothesis 2 was supported was Component 2 for POI 1 (missing their LH temporal lobe). Component 2 was previously linked to the processing of high frequencies in primary auditory cortex39 (see studies by Romaniat al.47 and Humphries et al.48 for the finding of tonotopic organization in human auditory cortex). This component manifests symmetrically in neurotypical brains.39,40 Although some evidence exists for right-ear advantage for processing high-frequency auditory information,49 most reports of left-hemispheric lateralization of auditory functions involved rapid frequency changes (on the order of 40 ms31), which likely reflect asymmetric sampling at higher-level processing stages,23 not processing that is carried out by primary auditory cortex. Therefore, this finding of increased representation of the high-frequency component in the individual missing their LH temporal lobe deserves further investigation. However, we found no evidence for increased representation of any other component, including the speech and music components.

Although the lack of significant differences between the participants of interest and the controls could be construed as a null finding, the robust presence of all the components and their similar spatial distribution are positive results that suggest preserved functional organization despite extensive early brain damage. In particular, (1) all components discovered by Norman-Haignere et al.39 were reliably detectable in each participant of interest; (2) their anatomical locations were similar to what is observed in typical brains, with the only exception being the weak responses measured for Component 3, which was previously associated with processing environmental sounds, but was shown to be less reliable relative to the other components in neurotypicals39,40; and finally (3) the anatomical locations of the components were stable across time, with the only exception being for Component 4, which was previously associated with the processing of pitched sounds and was less stable for POI 1 who lacks their left temporal lobe. However, pitch processing has been claimed to be lateralized to the right hemisphere,36 which would predict compensatory reorganization when the right temporal lobe is missing (i.e., in POI 2, not in POI 1).

Our results align with the lack of clear hemispheric asymmetries in the original study by Norman-Haignere et al.39 as well as its replication by Boebinger et al.40: all six components were roughly symmetric in their response profiles and spatial extent, with no significant hemispheric differences in the average weight for any of the components. Even the speech component was similarly robust in the left and right hemispheres, in contrast to some claims of left-lateralized speech responses in infancy.50,51,52,53,54 However, although typical brains do not show clear lateralization of the auditory components that we measured, some component(s) could in principle still show increased compensatory representation in the absence of the contralateral lobe. Such a pattern would suggest that, in neurotypicals, despite robustly bilateral responses, one lobe may be more important for some auditory function(s), or simply that both lobes are needed for proper functioning. Therefore, our findings (1) extend previous reports of similarly strong responses to different aspects of audition across the two hemispheres by demonstrating the independence of each hemisphere’s auditory cortex (i.e., similar organization regardless of the existence of the contralateral one), and (2) indicate that one auditory cortex, either in the left or the right hemisphere, may suffice for proper auditory function.

The finding that the RH auditory cortex can support speech perception aligns with reports of high-level language processing being successfully supported by the right hemisphere in some individuals with early left-hemisphere damage.5,11,55 Such individuals can develop linguistic abilities normally and exhibit no language difficulties as adults. If the (ontologically earlier-developing) speech perception abilities could not be supported by the right hemisphere (or at least not as well as by the left hemisphere), individuals with early left-hemisphere damage should inevitably exhibit delays in language acquisition and/or lasting deficits in language processing.

The apparent equipotentiality of the two hemispheres for auditory processing additionally suggests that any anatomical asymmetries in the auditory cortex are not necessary for normal cortical functioning. In particular, anatomical asymmetries have been reported in fetal and infant brains in auditory cortical areas, including Heschl’s gyrus and Planum temporale,56,57,58 as well as brain areas/tracts surrounding the auditory cortex (e.g., the arcuate fasciculus and superior temporal sulcus59,60,61). However, given that auditory processing can be supported by either hemisphere, these asymmetries do not appear to be critical for auditory function.

Limitations of the study

One potential limitation of the current study is that the control participants were younger than the POIs. If the auditory components get smaller with age, then a similarly sized component in the older POIs may, in fact, reflect an increase in size compared to neurotypical individuals their age. To explore this possibility, we examined our measures as a function of age in the controls and found that our brain measures of interest do not change with age, at least in our sample (STAR Methods section “age-dependence of the brain measures,” Figures S3 and S4; Tables S2 and S3), which alleviates this concern to some degree. However, our sample size is too small to draw conclusions about the relationship between our brain measures of interest and age in the general population. Other limitations have to do with the scope of the study. First, we focused on functional measures and restricted our analyses to cortical auditory processing. As a result, both structural (gray and white matter) measures and subcortical auditory responses should be examined in future studies. For example, how the auditory pathways may be affected in the lesioned hemispheres is not known. Some have argued that auditory information can travel directly to the STG from subcortical processing stations62; how the presence of such pathways may affect cortical organization in cases where parts of the STG are preserved remains to be discovered. Second, the paradigm that we used provides good coverage of the main functional auditory response components time-efficiently, which allowed us to examine the general organization of the auditory cortex, but is not designed to tax different kinds of auditory processes, which may be more likely to reveal differences between the POIs and controls. For instance, some effects of auditory spectrotemporal modulation28,63 or spatial hearing64,65 have been suggested to show hemispheric biases in typical brains and therefore should be explored in future studies. Third, the resolution of fMRI measurements may be too coarse to reveal differences, and future studies could use finer resolution approaches such as 7 Tesla imaging or intracranial recordings (if individuals with similar brain profiles ever need to undergo brain resection). Fourth, our findings apply only to cases of early stroke that do not cause detectable behavioral consequences, and more generally, should be extended to other individuals with early temporal lobe damage to evaluate the generalizability of the results.

Overall then, our results demonstrate that neurotypical-like organization of the auditory cortex can emerge in the absence of the contralateral temporal lobe (regardless of which hemisphere is affected), highlighting the independence and equipotentiality of the auditory cortex in the two hemispheres and thus, the internal redundancy in the human auditory cortical system.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Deposited data	
	
Processed brain data of the 3 Participants of Interest (POI) and the 12 neurotypical control participants	OSF	https://osf.io/qrx5n/	
	
Software and algorithms	
	
Original Code – MATLAB versions 2020a-2024b	OSF	https://osf.io/qrx5n/	
	
Other	
	
Experimental stimuli – 30 natural sounds from Norman-Haignere et al. 2015, Boebinger et al. 2021	OSF	https://osf.io/qrx5n/	

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Tamar Regev (tamarr@mit.edu), or by the other corresponding authors: Benjamin Lipkin (lipkinb@mit.edu) or Evelina Fedorenko (evelina9@mit.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

• Processed brain data have been deposited at Open Science Framework (OSF) - https://osf.io/qrx5n/ and is publicly available as of the date of publication, as specified in the key resources table. Participants did not consent to raw brain data release.

• All original code has been deposited at Open Science Framework (OSF) - https://osf.io/qrx5n/ and is publicly available as of the date of publication, as specified in the key resources table.

• Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Experimental model and study participant details

Participants of interest (POIs)

The first POI, henceforth referred to as POI 1 contacted professors at MIT Brain and Cognitive Sciences (BCS) in February 2016 volunteering to participate in studies of their brain, which they reported had no left temporal lobe. POI1 had never suffered any head traumas or injuries, but discovered this feature when an MRI scan was performed in 1987 when they were 25-years-old during treatment for depression. Additional scans were performed in 1988, 1998, and 2013 without any reported changes. Despite this supposedly congenital condition, POI 1 is highly educated, with an advanced professional degree, and reports no problems with vision, except nearsightedness (corrected with glasses), and had even studied Russian as a foreign language in adulthood, achieving high proficiency. POI 1 (right-handed, 54-years old at testing) participated in behavioral and fMRI testing at MIT in October 2016 and September 2019.

The second POI, henceforth referred to as POI 2 is POI 1’s sibling. During testing for some vision problems in 1981 when they were 17 years old, POI 2 discovered that they had no right temporal lobe. POI 2 (right-handed, 55-years-old at testing) participated in behavioral and fMRI testing at MIT in September 2019.

The third POI, henceforth referred to as POI 3 is the third sibling of POI 1 and POI 2. POI 3 has a neurotypical brain and serves as a close control to the siblings, discounting potential sources of variation from the control population outlined below. POI 3 (right-handed, 57-years-old at testing) participated in behavioral and fMRI testing at MIT in September 2019.

The POIs did not consent to publicly sharing their sex, gender, race, ancestry or socioeconomic status. However, the authors are not aware of any potential effect of any of these demographic details on the reported results.

The protocol for these studies was approved by MIT’s Committee on the Use of Humans as Experimental Subjects (COUHES). All participants gave written informed consent in accordance with the requirements of this protocol (number 2010000243).

Control participants

In addition to the three siblings, 12 neurotypical participants (mean age = 27.8, std = 4.1 years, 3 females, 11 right-handed and 1 ambidextrous, according to self-report) were recruited for fMRI testing at BCS. These participants were recruited from MIT and the surrounding Cambridge/Boston, MA community and were paid for their participation. All participants had normal hearing and vision. See Table S6 for further demographic details for control participants. Some participants did not share their race, and ancestry or socioeconomic status were not collected. However, the authors are not aware of any potential effect of any of these demographic details on the reported results.

These data were originally collected for the purpose of other studies and were re-analyzed here. The protocol for these studies was approved by MIT’s Committee on the Use of Humans as Experimental Subjects (COUHES). All participants gave written informed consent in accordance with the requirements of this protocol (number 1012004218).

Age-dependence of the brain measures

The age difference between the 12 neurotypical control participants (mean age = 27.8 years, std = 4.1) and the POIs (54, 55 and 57 years for POIs 1, 2 and 3, respectively) is a general limitation of our study. The most pressing concern caused by this age difference is whether there exists a negative relationship between the brain measures we used and age. Had such a negative correlation existed, it could in principle obscure a compensatory effect caused by the lesions of the POIs: the POIs are older and thus would have been expected to have a smaller auditory component representation, and together with a compensatory increase in representation due to the lesions, the result is the same size of representation between the POIs and the controls, as we found. We therefore directly tested whether a negative relationship existed in our dataset between the brain measures we used and the age of the control participants, and found no support for this argument, alleviating the concern above.

First, we tested the correlations between brain measures and age, including both the 12 young neurotypical controls, as well as POI 3, which is also neurotypical and has an age comparable to their siblings (Table S2; Figure S3). Importantly, as can be seen from the table, no p-value is lower than 0.1. Furthermore, out of 24 relevant values (2 measures: Response magnitude and Spatial Extent ×6 components x 2 hemispheres), 10 of the correlations are positive (42%) and 14 (58%) are negative, which suggests that there is no consistent relationship with age. Second, we tested the correlations between brain measures and age, including only the 12 young neurotypical controls, and excluding POI 3 because their older age is an outlier relative to the 12 younger controls (Table S3; Figure S4). There, 8 of the correlations are positive (33.3%) and 16 are negative (66.6%, Table S3). However, note that here, as in Table S2, some of these correlations are very small. The only p-values that are below 0.05 are, in fact, the positive correlation values: Response magnitude and Spatial extent of Component 6, left hemisphere (marked in green below), which would go against the concern we are testing here. There are only two more p-values which are below 0.1 (but above 0.05), and they are both negative (marked in orange below). In sum, these results do not indicate a consistently negative correlation with age. As a result, the argument that a negative correlation with age could obscure a positive relationship between activations and the existence of the lesion (compensation effects) is not supported.

Method details

Behavioral assessment for the POIs

Language assessment

To assess language skills, four standardized language assessment tasks were used: i) an electronic version of the Peabody Picture Vocabulary Test (PPVT-IV)66; ii) an electronic version of the Test for Reception of Grammar (TROG-2)67; and iii) the Western Aphasia Battery-Revised (WAB-R).68 PPVT-IV and TROG-2 target receptive vocabulary and grammar, respectively. In these tasks, the participant is shown sets of four pictures accompanied by a word (PPVT-IV, 72 trials) or sentence (TROG-2, 80 trials) and has to choose the picture that corresponds to the word/sentence by clicking on it. WAB-R68 is a more general language assessment for persons with aphasia. It consists of nine subscales, assessing 1) spontaneous speech, 2) auditory verbal comprehension, 3) repetition, 4) naming and word finding, 5) reading, 6) writing, 7) apraxia, 8) construction, visuospatial, and calculation tasks, and 9) writing and reading tasks.

General cognitive skills assessment

To assess general cognitive skills, two tasks were used: i) an electronic version of the Kaufman Brief Intelligence Test (KBIT-2),69 and ii) the 3-pictures version of the Pyramids and Palm Trees Test.70 The former consists of three subtests – two verbal (Verbal Knowledge and Riddles) and one non-verbal (Matrices) – and is used to assess general fluid intelligence. The Verbal Knowledge subtest consists of 60 items measuring receptive vocabulary and general information about the world; the Riddles subtest consists of 48 items measuring verbal comprehension, reasoning, and vocabulary knowledge; and the Matrices subtest consists of 46 items that involve both meaningful (people and objects) and abstract (designs and symbols) visual stimuli that require understanding of relationships among the stimuli. The Pyramids and Palm Trees test assesses non-verbal semantic cognition. The task consists of 52 trials. On each trial the participant is shown a test picture (e.g., an Egyptian pyramid) and two other pictures (e.g., a palm tree and a fur tree) and asked to choose the picture that is semantically related to the test picture (in this case, a palm tree is the correct answer). For both tests, the POI’s performance was evaluated against existing norms.

Stimuli & fMRI task design

An abridged version of the task used in Norman-Haignere et al., (2015) was used to evoke responses from participants to a selection of 30 2-s natural sounds (included in supplemental; osf.io/qrx5n/). These 30 sounds were chosen as the subset of the 165 sounds that were best able to identify the six components. This was done using a greedy algorithm to search for subsets of sounds that had a high but uncorrelated response variance across the components.40

Stimuli were presented during scanning in a “mini-block design,” in which each 2-s sound was repeated three times in a row. During scanning, stimuli were presented over MR-compatible earphones (Sensimetrics S14) at 75 dB SPL. Each stimulus was presented in silence, with a single fMRI volume collected between each repetition (i.e., “sparse scanning”; Hall et al.71). To encourage participants to pay attention to the sounds, either the second or third repetition in each “mini-block” was 8dB quieter (presented at 67 dB SPL), and participants were instructed to press a button when they heard this quieter sound. All sounds were presented diotically and thus we do not examine hemispheric differences in spatial coding.

Each run of the experiment included all 30 stimuli and lasted approximately 6.5 min. The POIs each completed six runs, and the control participants completed 6–10 runs. To confirm initial data quality, motion was quantified for each run of each participant. The mean RMS deviation of all runs across all participants was <1mm (0.18 ± 0.11mm for neurotypical participants; 0.22 ± 0.14mm for POIs), and the run-level motion parameters of the POIs did not differ from the neurotypical participants (two-sample t-test with unequal variance; t = 1.25; two-sided p = 0.22).

fMRI data acquisition

Structural and functional data were collected on a 3 Tesla, 32-channel head coil, Siemens Trio scanner at the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT. T1-weighted structural images were collected via 176 sagittal slices with 1mm isotropic voxels (TR = 2530ms, TE = 3.48ms). Functional BOLD data were acquired in 31 4mm thick near-axial slices acquired in the interleaved order (with 10% distance factor) using an EPI sequence with the following parameters: 90° flip angle, GRAPPA acceleration factor 2, 2.1 mm × 2.1 mm in-plane resolution, field of view of in the phase encoding (A > P) direction 200mm and matrix size 96 mm × 96 mm, TR = 2000ms and TE = 30ms. Prospective acquisition correction72 was used to adjust gradient position based on the participant’s motion from the previous TR. The first 10s of each run were excluded to allow for steady state magnetization.

Quantification and statistical analysis

fMRI preprocessing

Anatomical and functional data were preprocessed using FreeSurfer (https://surfer.nmr.mgh.harvard.edu), FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL), and supporting custom MATLAB scripts. Surface reconstructions were generated using FreeSurfer recon-all73 and the reconstructions of the lesions were confirmed visually by the authors. Motion correction was implemented using FSL MCFLIRT,74 and functional images were skull-stripped using FSL BET2.75 Functional images were then coarsely registered to their high-resolution anatomical counterparts using FSL FLIRT76 and fine-tuned using a boundary-based alignment algorithm referred to as BBRegister.77 Registrations were confirmed manually by the authors, especially in the most susceptible regions around the lesions of the siblings. Following registration, functional data were resampled from 3D volume space to 2D cortical surfaces using FreeSurfer mri_vol2surf and aligned to the FsAverage template brain using FLIRT and BBRegister. Finally, data were smoothed using a 3mm FWHM Gaussian kernel and downsampled to a 1.5 × 1.5mm flattened surface grid in MATLAB.

fMRI first-level modeling

Effects in each vertex of the surface grid were estimated using a General Linear Model (GLM). There was a separate regressor for each component. This regressor was computed by creating a boxcar function for each stimulus whose height was equal to the component’s response to that stimulus, as estimated in our prior studies. These boxcar functions were then convolved with the canonical hemodynamic response function (HRF). Solving this GLM calculated a beta weight for each component of each voxel and these estimates formed the basis of subsequent analyses. Since fMRI data are neither independent across time nor Gaussian distributed, the significance of these beta estimates was evaluated using a permutation test. For each voxel, a null distribution was estimated by randomly permuting the order of stimuli in the paradigm file, and re-running the analysis (n = 1000). The above process was originally performed using all runs, and was later repeated using only odd/even runs for subsequent stability analyses.

Definition of stimulus component weights

The natural sound component weights used in this study were defined via Norman-Haignere et al., (2015) using a hypothesis-free voxel decomposition of auditory cortex responses to a large collection of natural sounds (superset of sounds presented in this study). In voxel decomposition, voxels are approximated as a weighted sum of a small number of canonical response patterns. This approximation problem is ill-posed and must be constrained by additional statistical criteria, which was accomplished by searching for components that have a non-Gaussian weight distribution across voxels (see Norman-Haignere et al.39 for a detailed discussion). Once the component response patterns are known, the weights can be estimated using ordering least-squares regression, as described above, which requires much less data than computing the full decomposition (which depends upon higher-order statistics such as the skew and kurtosis which require large amounts of data to robustly measure). In the original study,39 such a decomposition yielded a set of six components, which explained >80% of the noise-corrected variance. These six components have since been replicated twice in two independent populations.40 We used the response patterns from the six components from39 to calculate the component weights reported here.

Definition of anatomical auditory ROIs

All analyses were restricted to an anatomically defined region of the broader auditory cortex, as in.40 This region was defined using the41 atlas based on a multi-modal parcellation of the human cerebral cortex, with the goal of broadly encompassing sound-responsive cortex. The parcels for the following regions were resampled to the surface grid space used in this study, and used to constrain the ROI for analysis: Primary Auditory Cortex, PeriSylvian Language Area, Superior Temporal Visual Area, Area 52, RetroInsular Cortex, Area PFcm, Area TA2, Area STGa, ParaBelt Complex, Auditory 5 Complex, Area PF Complex, Medial Belt Complex, Lateral Belt Complex, Auditory 4 Complex, and Para-Insular Area.

Note on lesions

All subsequent analyses were performed using only non-lesioned hemisphere data, constraining only RH for POI 1 and only LH for POI 2. Thus, all analyses of RH activity would evaluate POI 1 RH and POI 3 RH in relation to neurotypical RH, and all analyses of LH activity would evaluate POI 2 LH and POI 3 LH in relation to neurotypical LH.

Data reliability

To estimate the level of noise in the data of the POIs and compare it to the control participants, we computed split-half correlations (between odd and even runs) of the measured fMRI responses averaged across the voxels in the auditory cortex mask (STAR Methods) as well as for each component separately, average weighted due to component voxel weights (Figure S2). None of the POI values was significantly different from the controls according to linear mixed-effects model comparison (see section ‘statistical testing’ below, and Table S1).

Probabilistic map

For each of the three POIs and each of the 12 neurotypical control participants, the significant voxels (p < 0.05, uncorrected) responsive to each component within the auditory cortex ROI were extracted, and assigned a value of 1 if significant, and 0 otherwise. From these neurotypical-participant-level binary maps, the probabilistic map of the response profile was generated by calculating the mean of the binary maps. For example, if a voxel was significant in 6 of 12 participants, it’s value in the probabilistic map would be 0.5. Each of the POIs responses (binarized in the same way) were plotted onto this atlas to qualitatively identify a spatial correspondence in activity. For visualization purposed we plotted just the largest contiguous cluster of the binarized POIs response map (Figure 4).

Statistical testing

In order to compare the POI data to a control group, linear-mixed-effects (LME) models were used in several of the following analyses. We compared two LME models; Model 1 (M1) assumed that all data points including the POI and the controls belong to the same Gaussian distribution by regressing all data points on a fixed effects intercept and a random effect of participant. Model 2 (M2) assumed that the control participants belong to a Gaussian distribution and the POI datapoint has a bias relative to that distribution mean, by regressing all data points on a fixed effects intercept and another additive fixed effect variable just for the POI (implemented by assigning a categorical variable with a value of 1 for the POI and 0 for the controls and adding this value as a fixed effect), and a random effect of participant. To compare these two models we used two methods; first, since these two LME models were nested, we were able to compare them by means of a likelihood ratio test. We used MATLAB (version 2022a) to run the models and compare them (fitlme and compare procedures). Second, we computed the corrected Akaike Information Criterion (AICc) which is a correction of the AIC for a small sample size42:AIC=2k−2ln(Lˆ)

AICc=AIC+2k2+2kn−k−1,

where L is the maximized value of the likelihood function of the model and k is the number of estimated parameters of the model.

The model with the smaller AICc value is generally supported by the evidence. Furthermore, using the AICc scores for M1 and M2, we can calculate the relative likelihood of M1 with respect to M243:Rel_Lik_M1_M2=exp((AICc(M2)−AICc(M1))/2)

This value can be precisely interpreted as how much more probable M1 is than M2 given the data, similar to a Bayes Factor. We select this approach as it is similar to Bayes Factor calculation, but allows us to work with maximum-likelihood estimates as opposed to integrating over all possible model parameters, which is expensive to calculate, while still explicitly accounting for model complexity (in terms of # of parameters) and the small sample size (using the correction for AIC for small sample size). For each specific measure, we computed the relative likelihood value and reported the evidence using a standard scale (Kass and Raftery44; Table S4).

This procedure was used to compare values extracted from POI 1’s and POI 3’s RH to a set of values from the neurotypical RH, and values extracted from POI 2’s and POI 3’s LH to a set of values from the neurotypical LH.

Response magnitude and spatial extent

For each of the three POIs and each of the 12 neurotypical control participants, the mean effect size (beta; response magnitude) and the count of significant voxels (p < 0.05; spatial extent) of each component were calculated within the auditory parcel of each hemisphere. Using LME models (section ‘statistical testing’ above), each of the POIs’ statistics were compared to the neurotypical group. Results were FDR-corrected78 for the number of components (n = 6).

Spatial layout

For each component, the spatial response of each participant within the auditory ROI was extracted as a vector of the mean effect sizes within the 15 auditory parcels.41 Then, for each POI, Pearson’s linear correlation coefficient was calculated between the POI’s spatial component response, and the mean neurotypical spatial component response. These correlation coefficients were evaluated analytically for significance (alpha = 0.05). Subsequently, this process was repeated for each of the 12 neurotypical participants via their correlation with the mean of the remaining 11 neurotypical participants. Then, for each component, the correlation coefficients between each of the POIs and the neurotypicals were compared to the correlation coefficients within the neurotypical group via LME models (section ‘statistical testing’ above). Both correlation and Crawford analysis results were FDR-corrected for the number of components (n = 6).

Stability of component topographies over time

Using component activations (per parcel) calculated from only the odd or even runs of the experiment separately, the spatial correlation between these sets was calculated for each component of each participant in their relevant hemispheres using Pearson’s linear correlation coefficient. These correlations were evaluated for significance analytically (alpha = 0.05) as a measure of component spatial stability. The spatial stability of each of the POIs’ responses was then compared to the neurotypical group via LME models (section ‘statistical testing’ above). Both correlation and Crawford analysis results were FDR-corrected for the number of components (n = 6).

Supplemental information

Document S1. Figures S1–S4 and Tables S1–S6

Acknowledgments

We would like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT, and its support team (Steve Shannon and Atsushi Takahashi). We thank the participants of interest who agreed to participate in our study, as well as former and current EvLab members, especially Greta Tuckute and Niharika Jhingan, for their help with fMRI data collection and analysis. We thank Dr Leon Deouell, MD, for assisting with anatomical examination of the lesions. TIR was supported by the Zuckerman-CHE STEM Leadership Program and by the Poitras Center for Psychiatric Disorders Research. B.L. was supported by the 10.13039/100006919 MIT Presidential Fellowship. E.F. was supported by NIH awards R01-DC016607 , R01-DC016950 , and U01-NS121471 and research funds from the 10.13039/100019335 McGovern Institute for Brain Research , the Department of Brain and Cognitive Sciences, and the 10.13039/100018792 Simons Center for the Social Brain . The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author contributions

Formal analysis, T.I.R. and B.L. Visualizations, T.I.R. and B.L. Data collection, A.P., H.K., D.B., and S.V.N.-H. Design of experimental paradigm and analysis tools, S.V.N.-H. and D.B. Conceptualization, E.F., T.I.R., and B.L. Writing – original draft, T.I.R., B.L., and E.F. Writing – review & editing, all authors. Overall supervision, E.F.

Declaration of interests

The authors declare no conflicting interests.

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110548.
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