
==== Front
Brain
Brain
brainj
Brain
0006-8950
1460-2156
Oxford University Press UK

38426222
10.1093/brain/awae067
awae067
Original Article
AcademicSubjects/MED00310
AcademicSubjects/SCI01870
Atrophy network mapping of clinical subtypes and main symptoms in frontotemporal dementia
https://orcid.org/0000-0001-7572-6162
Chu Min Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Jiang Deming Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Li Dan Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Yan Shaozhen Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Liu Li Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Nan Haitian Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Wang Yingtao Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Wang Yihao Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Yue Ailing Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

https://orcid.org/0000-0001-5147-3068
Ren Liankun Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Chen Kewei School of Mathematics and Statistics, Banner Alzheimer’s Institute, University of Arizona, Arizona Alzheimer’s Consortium, Arizona State University, Tempe, AZ 85014-3666, USA

https://orcid.org/0000-0001-9116-1376
Rosa-Neto Pedro McGill Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Montreal H4H 1R3, Canada

Lu Jie Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

Wu Liyong Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
the Frontotemporal Lobar Degeneration Neuroimaging Initiative

Correspondence to: Liyong Wu Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, China E-mail: wmywly@hotmail.com
Correspondence may also be addressed to: Jie Lu Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University, Beijing, China E-mail: imaginglu@hotmail.com
Pedro Rosa-Neto McGill Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Montreal H4H 1R3, Canada E-mail: pedro.rosa@mcgill.ca
Min Chu, Deming Jiang and Dan Li contributed equally to this work.

9 2024
01 3 2024
01 3 2024
147 9 30483058
10 10 2023
18 12 2023
10 2 2024
02 8 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.
2024
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Abstract

Frontotemporal dementia (FTD) is a disease of high heterogeneity, apathy and disinhibition present in all subtypes of FTD and imposes a significant burden on families/society. Traditional neuroimaging analysis has limitations in elucidating the network localization due to individual clinical and neuroanatomical variability.

The study aims to identify the atrophy network map associated with different FTD clinical subtypes and determine the specific localization of the network for apathy and disinhibition.

Eighty FTD patients [45 behavioural variant FTD (bvFTD) and 35 semantic variant progressive primary aphasia (svPPA)] and 58 healthy controls at Xuanwu Hospital were enrolled as Dataset 1; 112 FTD patients including 50 bvFTD, 32 svPPA and 30 non-fluent variant PPA (nfvPPA) cases, and 110 healthy controls from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) dataset were included as Dataset 2. Initially, single-subject atrophy maps were defined by comparing cortical thickness in each FTD patient versus healthy controls. Next, the network of brain regions functionally connected to each FTD patient’s location of atrophy was determined using seed-based functional connectivity in a large (n = 1000) normative connectome.

Finally, we used atrophy network mapping to define clinical subtype-specific network (45 bvFTD, 35 svPPA and 58 healthy controls in Dataset 1; 50 bvFTD, 32 svPPA, 30 nfvPPA and 110 healthy controls in Dataset 2) and symptom-specific networks [combined Datasets 1 and 2, apathy without depression versus non-apathy without depression (80:26), disinhibition versus non-disinhibition (88:68)]. We compare the result with matched symptom networks derived from patients with focal brain lesions or conjunction analysis.

Through the analysis of two datasets, we identified heterogeneity in atrophy patterns among FTD patients. However, these atrophy patterns are connected to a common brain network. The primary regions affected by atrophy in FTD included the frontal and temporal lobes, particularly the anterior temporal lobe. bvFTD connects to frontal and temporal cortical areas, svPPA mainly impacts the anterior temporal region and nfvPPA targets the inferior frontal gyrus and precentral cortex regions. The apathy-specific network was localized in the orbital frontal cortex and ventral striatum, while the disinhibition-specific network was localized in the bilateral orbital frontal gyrus and right temporal lobe. Apathy and disinhibition atrophy networks resemble known motivational and criminal lesion networks, respectively. A significant correlation was found between the apathy/disinhibition scores and functional connectivity between atrophy maps and the peak of the networks.

This study localizes the common network of clinical subtypes and main symptoms in FTD, guiding future FTD neuromodulation interventions.

Chu et al. identify specific atrophy networks associated with different clinical subtypes of frontotemporal dementia, and with apathy and disinhibition. The findings will aid the selection of future neuromodulation targets for the treatment of frontotemporal dementia.

apathy
disinhibition
frontotemporal dementia
atrophy network map
National Natural Science Foundation of China 10.13039/501100001809 82271464
==== Body
pmcIntroduction

Frontotemporal dementia (FTD) encompasses behavioural variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA) and non-fluent variant PPA (nfvPPA).1,2 Apathy and disinhibition were the common features in FTD, especially in bvFTD subtype,1 which impose a heavy burden on families and society and currently with no effective treatment. Neuromodulation holds promise as a potential treatment, but the target localization of the symptoms remains elusive.3 Traditional neuroimaging markers, such as cortical thickness, grey matter volume/glucose metabolism, or white matter integrity, have found that single brain regions associated with apathy and disinhibition are mainly localized in the frontal lobe and basal ganglia.4-9 However, it has limitations in elucidating the individualized heterogeneity of symptom-related brain regions and neural circuits.5

One promising solution to considering individual heterogeneity is to map the clinical symptoms to brain networks. Complex behaviour requires the integration of multiple interconnected brain regions and a focal lesion in any of these regions can lead to similar symptoms.10 Using human connectome data, it is possible to identify networks associated with focal lesions through a method known as lesion network mapping.10,11 Similarly, network mapping can be used to investigate the relationship between brain atrophy and specific symptoms.12 The atrophy network mapping method can use individual structural MRI and connectome data to identify the single-subject atrophy network and then determine the symptom-specific network map through comparison between the individuals with and without symptoms. This method has been employed for network localization in conditions such as alien limb in cortico-basal syndrome (CBS),13 memory decline and delusions in Alzheimer’s disease (AD)12 and anxiety in multiple sclerosis.14

Previous study only investigates the atrophy map or network map using only single modal image (structural MRI or functional MRI data), however, the atrophy network map [brain regions connected with atrophy map calculated simultaneously by structural MRI (sMRI) and functional MRI (fMRI) data] in FTD remains elusive. In this study, we used the patient data from two datasets including Xuanwu hospital and Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) to determine the network localization of clinical subtype and main symptoms (apathy and disinhibition) in FTD using the ‘atrophy network mapping’ method.

In addition, in previous studies on atrophy network mapping (such as memory in AD, alien limb in CBS, etc.), the spatial correlation of atrophy mapping networks with lesion mapping networks can increase the reliability and persuasiveness of the atrophy network mapping results.12,13 However, no studies on lesion network mapping for apathy and disinhibition were available. Therefore, network mapping results, which include apathy or disinhibition, can be chosen to compare. First, for apathy, it is well known that both apathy and anhedonia are considered common motivational deficiency syndromes related to a variety of brain diseases and that there are common neuromechanisms between the two syndromes.15,16 One network conjunction analysis reported peak coordinates of the motivational deficit network (including the apathy lesion network),17 which is suitable for comparison and validation with the apathy atrophy network in our study. Second, disinhibition was related to criminal behaviour. Studies have shown that 37.4% of patients with bvFTD and 27% of patients with svPPA exhibit criminal behaviours, including sexual advances, theft, public urination, violence, and traffic violations, even homicide.18-22 The underlying substrates for such criminal behaviours are disinhibition, impulsivity and reward/punishment dysfunction.18 Criminal behaviour is an extreme manifestation of disinhibition, possibly involving the same neural circuits, thus we intend to select the mapping of criminal lesion networks23 for comparison with the disinhibition network.

Materials and methods

Participants

The first dataset was from Xuanwu Hospital, Capital Medical University, including 80 FTD patients and 58 healthy controls who underwent T1 scanning. Patients were diagnosed of probable bvFTD based on the 2011 diagnostic criteria,1 while imaging supported svPPA was based on the 2011 diagnostic criteria for PPA.2 Participants with right lateralized anterior temporal lobe atrophy were excluded. All patients were diagnosed as ‘probable bvFTD’ or ‘imaging supported svPPA’. The healthy controls group, matched for age and gender, had no complaints of cognitive decline, depression or anxiety. Furthermore, they did not demonstrate apathy and disinhibition and they performed within the normal range on neuropsychological tests [Mini-Mental State Examination score (MMSE) ≥ 24, Frontotemporal Lobar Degeneration-Clinical Dementia Rating (FTLD-CDR) score of 0].

The second dataset was from the FTLDNI. It included 112 FTD patients who underwent T1 scanning, including 50 bvFTD, 32 svPPA, 30 nfvPPA cases and 110 healthy controls. No participant with right lateralized anterior temporal lobe atrophy was included. All patients were diagnosed as ‘probable bvFTD’1 or ‘imaging supported svPPA/nfvPPA’.2 FTLDNI, funded by the National Institute on Aging (NIA), aims to identify neuroimaging patterns and analysis methods for tracking frontotemporal lobar degeneration (FTLD) and assess the diagnostic value of imaging in conjunction with other biomarkers. Visit https://4rtni–ftldni.ini.usc.edu/ for the latest information on participants and protocols.

Of 80 patients with FTD in Dataset 1, 11 patients carried genetic mutations, including eight with mutations on the MAPT gene, including p.P301L (c.1907C>T), p.V337M (c.2014G>A), p.N296N (c.1839T>C), p.R5C (c.13C>T) and p.D54N (c.160G>A), two carried a GRN mutation and one carried expansion of hexanucleotide GGGGCC repeats (G4C2) in the C9orf72 gene. The matched control group did not carry any pathogenetic genes. No information about pathogenic genes was found in Dataset 2. Participants with right lateralized anterior temporal lobe atrophy were excluded from our study.24,25

MRI acquisition and analysis

MRI scanning parameters in Dataset 1

The image data in Xuanwu Hospital were acquired in a 3.0 T time-of-flight MRI scanner (SIGNA MR, GE Healthcare). The parameters were as follows: 3D-T1 images were acquired using the magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence: repetition time/echo time/inversion time = 6.9/3.0/450 ms, flip angle = 12°, field of view (FOV) = 256 mm × 256 mm, acquisition matrix = 256 × 256, signal acquisitions = 1, slice thickness = 1 mm, slice gap = 1 mm and number of slices = 192, resulting in a 3D dataset.

MRI scanning parameters in Dataset 2

Funded by the NIA, the FTLDNI was launched in 2010. Its primary objectives are to identify neuroimaging modalities and analytical methods for monitoring the progression of FTLD and to assess the diagnostic value of imaging in comparison to other biomarkers. This collaborative project involves three sites across North America. Visit https://4rtni–ftldni.ini.usc.edu/ for the most up-to-date information on participation and protocols. In August 2023, data were accessed and downloaded from the FTLDNI database via the LONI platform. The study included individuals with T1-weighted MRI scans who met the inclusion criteria for FTD patients and clinically healthy controls at each clinical visit. The diagnostic criteria for bvFTD patients were based on the guidelines established by the Frontotemporal Dementia Consortium,1 svPPA and nfvPPA were based on diagnostic criteria of PPA.2 All participants provided informed consent, obtained according to the Declaration of Helsinki. The research protocol was approved by the institutional review boards at all sites.

MRI processing

Quantitative morphometric analysis was performed using FreeSurfer v6.0.26 After spatial normalization, intensity normalization and skull stripping, the resulting volume was segmented into grey matter, white matter and CSF. Subsequently, a deformable surface algorithm was used to identify the pial surface. Cortical thickness was determined by measuring the difference between the white matter and pial surfaces at 160 000 points (vertices). The individual brains were then reconstructed and registered to average spherical space, allowing cortical locations to be accurately matched across individuals.

Single-subject atrophy maps

First, a vertex-wise general linear model (GLM) was constructed for cortical thickness based on cognitively normal individuals from each dataset, with the independent variable as age and gender, and the dependent variable as the cortical thickness (Fig. 1A). This GLM establishes the impacts of age and gender, which will serve as covariates for the next step. Next, using the beta maps of age and gender, as well as the residual maps from these standard models, w-scores (w-scores are z-scores adjusted for covariates, such as age and gender) were calculated for each patient’s cortical thickness at each vertex (Fig. 1B). The formula used to calculate w-scores is as follows: w-score = (actual value − expected value)/RSD. Here, the ‘actual cortical thickness’ represents the patient’s cortical thickness, the ‘expected cortical thickness’ is derived from the control GLM, and ‘RSD’ stands for the residual standard deviation of the GLM. The atrophy w-maps were binarized at w-score < −2, corresponding to two standard deviations (SD) below the mean of the controls, controlling for age and gender. This is the atrophy map for a given subject.

Figure 1 Workflow of individual brain atrophy mapping and network mapping. (A) Using Freesurfer, the cortical thickness of healthy controls is computed. A standard general linear model (GLM) for cortical thickness is generated based on age and sex. Each patient’s cortical thickness (CT) is compared to the standard model to generate a vertex-wise map of cortical atrophy, referred to as the individual brain atrophy map. (B) The individual brain atrophy map is mapped onto a functional MRI dataset of 1000 healthy individuals. This generates an individual atrophy network map, indicating the functional regions connected with the patient’s atrophy map. The network map overlap among patients indicates the percentage of patients whose atrophy is functionally connected to the same regions. HC = healthy control.

Atrophy network mapping

Next, an ‘atrophy network map’ was constructed for each patient, defined as brain regions functionally connected to the individual atrophy maps. The individual atrophy maps in surface space from each hemisphere were combined and transformed into MNI space (Fig. 1A). Using publicly available normative functional connectome data from 1000 healthy individuals from the Genome superstruct Project (GSP),27 the average blood oxygenation level-dependent (BOLD) time series was computed for all voxels in the individual atrophy maps. Pearson correlations were then computed between the average time series from each individual atrophy map and the BOLD time series for each voxel across the whole brain. The obtained R-values were subjected to a transformation to achieve a normal distribution using Fisher’s r-to-z transformation. These transformed values were then used to perform a one-sample voxel-wise t-test against the standard connectome dataset of 1000 individuals, ultimately generating an unthresholded atrophy network t-map (Fig. 1B). For visualization purposes, the atrophy network maps of each patient were thresholded and binarized at a t-level of ±7 [corresponding to voxel-wise family-wise error (FWE)-corrected P < 1 × 10−6]. Subsequently, all thresholded atrophy network maps from the patients were overlaid to identify regions connected to most patient atrophy regions (Fig. 1B).

Comparison atrophy network maps in FTD versus controls

The unthresholded atrophy network maps of FTD patients were compared to atrophy network maps constructed from age-matched cognitively healthy controls. The same method, as described above for individual atrophy maps, was used to define atrophy in these control groups, with each subject compared to the standard model and the resulting w-scores binarized (w < −2). Group-level t-tests were conducted to compare FTD and control groups on each voxel of the atrophy network maps, and statistical non-parametric mapping (SnPM13, http://www.nisox.org/Software/SnPM13) was used for permutation testing and correction for multiple comparisons (to correct for type I errors, 10 000 permutations, FWE-corrected P < 0.05), with age, sex and education added as covariates.

Atrophy network mapping of apathy/disinhibition symptoms

Voxel-wise analysis and comparison of brain atrophy networks were conducted between FTD patients with and without symptoms. Apathy/disinhibition assessment was obtained from the Neuropsychiatric Inventory (NPI) evaluation in two datasets. In the Xuanwu Hospital dataset, 36 FTD patients were missing NPI data and were excluded from the subsequent analysis. First, the apathy/disinhibition-specific atrophy mapping network analysis was conducted in two datasets separately. However, we only found the orbital cortex and ventral striatum network in the FTLDNI dataset (Supplementary Fig. 1). The demographic data of group comparison are shown in Supplementary Tables 1–4. To ensure a relatively larger sample, we then merged the data. After merging the data, 121 patients who reported apathy within 6 months of the baseline MRI scan, were classified into the apathy group, while the non-apathy group consisted of patients who never reported apathy throughout the study period (n = 35). To account for the potential confounding effect of depression, patients with comorbid depression were also excluded. The final analysis included an apathy group (n = 80) and a non-apathy group (n = 26). For disinhibition, 88 patients were included in the disinhibition group and 68 patients in the non-disinhibition group. Demographics and clinical variable data are shown in Supplementary Tables 5 and 6. To investigate the hypothesis that the brain networks to apathy and disinhibition may vary across subtypes, we conducted the apathy/disinhibition analysis separately in the bvFTD, svPPA and nfvPPA subtypes. The demographics and clinical variable data are shown in Supplementary Tables 7–12.

A voxel-wise two-sample t-test was performed to compare the brain atrophy network differences between patients with and without apathy, with age, sex and years of education as covariates. In each analysis, SnPM13 was used for permutation testing with multiple comparison corrections (10 000 permutations, FWE-corrected P < 0.05).

Comparing apathy/disinhibition atrophy networks

We directly compared our atrophy network mapping results for apathy and disinhibition with networks for these symptoms derived from patients with focal lesions using lesion network mapping, or conjunction region using conjunction analysis. We defined conjunction or lesion network mapping by generating a 4-mm spherical seed at the peak network mapping overlap location for the motivational network (MNI coordinates −16, 14, −2)17 and criminal network (MNI coordinates 2, 64, 6)23 from prior studies. We defined atrophy network maps by generating a 4-mm spherical seed at the peak grey matter coordinate for each analysis (apathy atrophy network, MNI coordinates −8, 6, 2; disinhibition atrophy network, MNI coordinates 64, −32, −2). We then conducted seed-based functional connectivity to determine the BOLD functional MRI time course correlations between each seed location and all other brain voxels. Finally, we measured the spatial correlation (excluding voxels outside of the MNI brain mask) between the symptom-specific atrophy network mapping and lesion-network mapping/conjunction as a measure of similarity.

Additionally, a linear regression analysis was conducted to investigate the association between individual NPI apathy/disinhibition severity scores and the functional connectivity strengths between the individual’s brain atrophy map and the apathy/motivation or disinhibition/criminal seeds. Furthermore, Frontal Behavioral Inventory (FBI) apathy/disinhibition scores was also used as sensitivity validation.

Results

Demographic data

Eighty FTD patients were enrolled in the Xuanwu dataset. Among these patients, 75% exhibited apathy symptoms, 29.5% exhibited disinhibition symptoms, and 36.4% exhibited signs of depression. The NPI assessment in the FTD group scores an average of 1.68 ± 1.16 for apathy, 0.55 ± 0.99 for disinhibition and 0.68 ± 1.01 for depression. The FBI scale scored an average of 13.60 ± 9.33 for apathy and 5.99 ± 5.61 for disinhibition. Detailed demographic data in all subgroups from Xuanwu Hospital are provided in Table 1.

Table 1 Demographics in dataset 1 of Xuanwu hospital

Dataset 1	Xuanwu hospital	
Total FTD	bvFTD	svPPA	HC	P
(Total FTD versus HC)	
Case	80	45	35	58	–	
Age	61.83 ± 6.69	61.60 ± 6.99	62.11 ± 6.38	62.43 ± 8.05	0.755	
Gender (F)	45 (56.3%)	27 (60.0%)	18 (51.4%)	32 (55.2%)	0.900	
Education	10.36 ± 3.78	10.82 ± 4.27	9.77 ± 3.00	11.12 ± 2.93	0.205	
Apathy	33/44 (75.0%)	30/41 (73.2%)	3/3 (100.0%)	0	<0.001	
NPI Severity	1.68 ± 1.16	1.66 ± 1.18	9.77 ± 3.00	0	<0.001	
Disinhibition	13/44 (29.5%)	13/41 (31.7%)	0/3 (0.0%)	0	<0.001	
NPI severity	0.55 ± 0.99	0.59 ± 1.02	0	0	<0.001	
Depression	16/44 (36.4%)	15/41 (36.6%)	0/3 (0.0%)	0	<0.001	
NPI severity	0.68 ± 1.01	0.68 ± 1.01	0.67 ± 1.16	0	<0.001	
NPI informant	–	–	–	–	–	
Spouse/children	77	44	33	55	–	
Siblings/other relatives	3	1	2	3	–	
Friends/neighbours/hired caregivers	0	0	0	0	–	
MMSE	15.78 ± 7.39	15.16 ± 8.19	16.60 ± 6.23	28.64 ± 1.46	<0.001	
FBI total	19.58 ± 13.92	25.51 ± 13.48	11.94 ± 10.43	0	<0.001	
FBI apathy	13.60 ± 9.33	17.49 ± 9.21	8.60 ± 6.82	0	<0.001	
FBI disinhibition	5.99 ± 5.61	8.00 ± 5.72	3.40 ± 4.31	0	<0.001	
CDR-global	1.13 ± 0.64	1.37 ± 0.69	0.83 ± 0.38	0	<0.001	
bvFTD = behavioural variant frontotemporal dementia; CDR-global = Clinical Dementia Rating—Global Score; FBI = Frontotemporal Behavior Inventory; FTD = frontotemporal dementia; HC = healthy controls; MMSE = Mini-Mental State Examination; NPI = Neuropsychiatric Inventory; svPPA = semantic variant primary progressive aphasia.

One hundred and twelve FTD patients and 110 healthy controls were included in the FTLDNI dataset. In the FTD group, 78.6% of patients exhibited apathy, 67% exhibited disinhibition and 30.5% exhibited depression. The NPI assessment in the FTD group scored an average of 1.53 ± 1.05 for apathy, 1.31 ± 1.09 for disinhibition and 0.43 ± 0.71 for depression. Detailed demographic data from all FTLDNI subgroups are provided in Table 2.

Table 2 Demographics in Dataset 2 of frontotemporal lobar degeneration neuroimaging initiative (FTLDNI)

Dataset 2		FTLDNI	
Total FTD	bvFTD	svPPA	nfvPPA	HC	P
(Total FTD versus HC)	
Case	112	50	32	30	110	–	
Age	63.67 ± 7.39	61.31 ± 6.86	63.31 ± 6.49	68.00 ± 7.42	62.96 ± 7.38	0.477	
Gender (F)	49 (43.8%)	18 (36%)	14 (43.8%)	17 (56.7%)	61 (55.5%)	0.081	
Education	15.72 ± 2.89	15.33 ± 3.12	16.26 ± 2.79	17.45 ± 1.85	17.45 ± 1.85	<0.001	
Apathy	88 (78.6%)	48 (96.0%)	26 (81.3%)	14 (46.7%)	0	<0.001	
NPI severity	1.53 ± 1.05	2.10 ± 0.84	1.28 ± 0.89	0.83 ± 1.02	0	<0.001	
Disinhibition	75 (67.0%)	44 (88.0%)	22 (68.8%)	9 (30.0%)	0	<0.001	
NPI severity	1.31 ± 1.09	1.92 ± 0.94	1.19 ± 1.00	0.43 ± 0.73	0	<0.001	
Depression	34 (30.4%)	14 (28.0%)	12 (37.5%)	8 (26.7%)	0	<0.001	
NPI severity	0.43 ± 0.71	0.38 ± 0.67	0.56 ± 0.80	0.37 ± 0.67	0	<0.001	
MMSE	23.88 ± 5.80	23.76 ± 4.69	24.13 ± 6.31	29.36 ± 0.77	29.36 ± 0.77	<0.001	
CDR-global	0.84 ± 0.55	1.18 ± 0.56	0.64 ± 0.32	0.50 ± 0.42	0	<0.001	
bvFTD = behavioural variant frontotemporal dementia; CDR-global = Clinical Dementia Rating—Global Score; FTD = frontotemporal dementia; HC = healthy controls; MMSE = Mini-Mental State Examination; NPI = Neuropsychiatric Inventory; svPPA = semantic variant primary progressive aphasia.

Heterogeneous individual atrophy maps in FTD

Dataset 1

In the Xuanwu Hospital overall FTD group, 84% (67/80) of patients showed bilateral frontal temporal lobe atrophy [Fig. 2A(i)]; in the bvFTD group, 71% (32/45) of patients showed atrophy in the frontal and temporal lobes [Fig. 2A(ii)]; and in the svPPA group, 100% (35/35) of patients showed bilateral temporal lobe atrophy [Fig. 2A(iii)].

Figure 2 Atrophy network mapping results in FTD patients. (A) The percentage of overlap (w-score < −2) in the brain atrophy maps for each participant. (B) The percentage of overlap in the brain atrophy network maps for patients in the same locations. (C) The voxel-wise t-test comparisons of brain atrophy network maps between patients and healthy controls (10 000 simulations, voxel-wise FWE-corrected P < 0.05). Dataset 1: [A(i)–C(i)] frontotemporal dementia (FTD); [A(ii)–C(ii)] behaviour variant FTD (bvFTD); [A(iii)–C(iii)] semantic variant primary progressive aphasia (svPPA). Dataset 2: [A(iv)–C(iv)] FTD; [A(v)–C(v)] bvFTD; [A(vi)–C(vi)] svPPA; [A(vii)–C(vii)] non-fluent variant PPA (nfvPPA).

Dataset 2

In the FTD group, 68% (76/112) of patients showed temporal lobe atrophy, with a left-side predominance[Fig. 2A(iv)]; in the bvFTD group, 78% (39/50) of patients showed atrophy in the frontal and temporal lobes [Fig. 2A(v)]; in the svPPA group, 100% (32/32) of patients showed anterior temporal lobe atrophy [Fig. 2A(vi)]; and in the nfvPPA group, 70% (21/30) of patients showed atrophy in the precentral gyrus, with a left-side predominance [Fig. 2A(vii)].

Atrophy network mapping identifies a common FTD atrophy network

While the locations of atrophy were heterogeneous, the network mapping was consistent across both groups. All FTD patients (80/80) in Dataset 1 and 97% (109/112) of patients in Dataset 2 had atrophy functionally connected to common brain regions in the frontal and temporal lobes [Fig. 2B(i and iv)]. There were some differences noted in the atrophy network mapping results between Dataset 1 and Dataset 2, with a higher percentage of patients showing atrophy connected to anterior temporal lobes in Dataset 2. FTD patients showed differences in frontal-temporal lobes in both datasets [Fig. 2C(i and iv)]. All bvFTD patients (45/45) in Dataset 1 and 98% (49/50) of patients in Dataset 2 were functionally connected to the frontal and temporal cortical areas [Fig. 2B(ii and v)]. bvFTD patients showed differences mainly in the frontal and temporal brain regions [Fig. 2C(ii and v)]. All svPPA patients (35/35) in Dataset 1 and all (32/32) patients in Dataset 2 showed that the atrophy map was functionally connected to the anterior temporal region [Fig. 2B(iii and vi)], while it showed differences mainly in the anterior temporal brain region [Fig. 2B(iii and vi)]. In nfvPPA patients, 100% (30/30) of patient atrophy in Dataset 2 was functionally connected to the inferior frontal gyrus and precentral cortex regions [Fig. 2B(vii)]. Differences were primarily observed in the inferior frontal gyrus and precentral cortex brain regions [Fig. 2C(vii)].

Apathy/disinhibition atrophy mapping networks and lesion/conjunction network mapping are similar

The apathy atrophy network [Fig. 3A(i)] included the bilateral orbitofrontal cortex and ventral striatum analysed in merged data of the FTD patients. The network mapping based on the peak location of the motivation network showed similarity to the apathy brain atrophy network map [Fig. 3A(ii)], with a spatial correlation of r = 0.83 [Fig. 3A(iii)]. In addition, the apathy atrophy network analysed in FTD patients of FTLDNI (Supplementary Fig. 1) and in the bvFTD subtype of pooled data (Supplementary Fig. 2), showed a similar network pattern in bilateral orbitofrontal cortex and ventral striatum.

Figure 3 Brain atrophy network mapping of apathy/disinhibition in FTD. The atrophy network map for apathy (10 000 simulations, voxel-wise FWE-corrected P < 0.05), [A(i)] was similar to the network map obtained using peak coordinates from a conjunction analysis of motivation-related regions [A(ii)], with a spatial correlation of R = 0.83 [A(iii)]. The atrophy network map for disinhibition (10 000 simulations, voxel-wise FWE-corrected P < 0.05) [B(i)] was similar to the criminal lesion network map [B(ii)] with a spatial correlation of R = 0.71 [B(iii)]. FTD = frontotemporal dementia; FWE = family-wise error.

The disinhibition atrophy network map was identified [Fig. 3B(i)] and included the right orbitofrontal gyrus, right temporal gyrus and left orbitofrontal gyrus. Mapping using the peak location of the disinhibition atrophy network and the peak location of the criminal lesion network revealed overlap distributions in the orbitofrontal and temporal cortices [Fig. 3B(ii)], with a spatial correlation of r = 0.71 [Fig. 3B(iii)].

Comparison of symptom and non-symptom group

The functional connectivity between brain atrophy maps and the peak location of the apathy atrophy network [P < 0.001, Fig. 4A(i)] or the motivation-related network [P = 0.002, Fig. 4A(ii)] was significantly higher in the apathy group. Multivariable regression analysis revealed that functional connectivity between brain atrophy maps and the peak location of the apathy atrophy network (r = 0.34, P < 0.001) or the motivation-related network (r = 0.31, P < 0.001) was correlated with the NPI apathy severity score [Fig. 4A(iii)]. And multivariable regression analysis also revealed that functional connectivity between brain atrophy maps and the peak location of the apathy atrophy network (r = 0.42, P < 0.001) or the motivation-related network (r = 0.49, P < 0.001) was correlated with the FBI apathy score [Fig. 4A(iv)].

Figure 4 Differences in functional connectivity strength between peak location of symptom-related brain network mapping and brain atrophy maps. Comparison of functional connectivity between the frontotemporal dementia (FTD) brain atrophy map and peak location of apathy brain network mapping [A(i)] or motivation-related brain network mapping [A(ii)]. The bar graph represents the differences between the apathy and non-apathy groups. Regression analysis of the functional connectivity mentioned above and Neuropsychiatric Inventory (NPI) apathy severity scores [A(iii)] or Frontal Behavioral Inventory (FBI) apathy scores [A(iv)]. Comparison of functional connectivity between the FTD brain atrophy map and peak location of disinhibition brain network mapping [B(i)] or criminal lesion network mapping [B(ii)]. The bar graph represents the differences between the disinhibition and non-disinhibition groups. Regression analysis of the functional connectivity mentioned above and NPI disinhibition severity scores [B(iii)] or FBI disinhibition scores [B(iv)].

Similarly, the functional connectivity between brain atrophy maps and the peak location of the disinhibition atrophy network [P < 0.001, Fig. 4B(i)] or the criminal lesion network [P = 0.006, Fig. 4B(ii)] was significantly higher in the disinhibition FTD group. Multivariate regression analysis showed that the functional connectivity between brain atrophy maps and the peak location of the disinhibition atrophy network was correlated with the NPI disinhibition severity score [r = 0.29, P < 0.001, Fig. 4B(iii)]. However, no other correlation was found between criminal lesion network and NPI disinhibition severity score/FBI disinhibition score, disinhibition atrophy network and FBI disinhibition score [Fig. 4B(iii and iv)].

Discussion

In this study, we used atrophy network mapping to localize different clinical subtype and main symptoms (apathy and disinhibition) in FTD within specific functionally connected brain networks, and matching apathy/disinhibition network to symptom-specific brain networks derived from patients with focal brain lesions and through conjunction analysis. Our study is the first to identify a common atrophy mapping network (brain regions connected with atrophy map) in FTD. This study revalidated the feasibility of the brain atrophy network mapping method in FTD and provided scientific evidence for the theory of the apathy network in the bilateral orbitofrontal gyri and ventral striatum, as well as the disinhibition network in right orbitofrontal gyrus, right temporal gyrus and left orbitofrontal gyrus.

Advantages of atrophy network mapping

The individual heterogeneity between brain atrophy locations and symptoms presents a challenge in establishing brain–behaviour relationships. Compared to traditional methods, which simply conducted a single modal image correlate analysis,12 the innovative aspect of this study lies in the combination of structural MRI and fMRI data to realize a common network despite the heterogeneity of individual atrophy maps. The conclusion is based on networks functionally connected to the brain atrophy regions, whereas traditional methods can only identify relevant single brain regions. Lesion network mapping offers causal explanations for the occurrence of focal brain lesions and symptoms.10 Brain atrophy network mapping needs to be compared with lesion network mapping results to confirm its reliability. In this study, the atrophy/disinhibition network mapping matches the previous motivational/criminal network, demonstrating the reliability of the results. The mechanism behind network localization remains unclear, but one possibility is that clinical symptoms may arise from functional impairments in connected but unaffected brain regions, or the complex interaction of symptoms within the entire network rather than any specific location within the network.

Heterogeneity of individual atrophy maps in FTD

However, the overlap of the atrophy map of FTD did not reach 100%, likely due to the strong heterogeneity in the pathology, genes and clinical phenotypes of FTD. FTD pathology involves different types of pathologies such as Tau, TDP-43 and FUS, with more than 20 identified pathogenic genes, most commonly including microtubule-associated protein tau (MAPT), progranulin (GRN) and C9orf72. Clinical subtypes can be classified into behavioural variants and language variants, with different pathological and genetic types corresponding to specific patterns of brain damage.28,29 As a result, within the same clinical syndrome, there may be different genotypes and pathologies, resulting in a lower overlap of atrophy maps compared to other neurodegenerative diseases.

Localizing different FTD subtypes to brain networks

The brain atrophy network maps of FTD patients in both datasets were similar, demonstrating the reliability of the two datasets. The overlap of network maps reached almost 100%. This study identified that atrophy maps of bvFTD were functionally connected to the frontal and temporal cortical areas, svPPA was functionally connected to the anterior temporal region, and nfvPPA was functionally connected to the inferior frontal gyrus and precentral cortex regions. A previous network mapping analysis based on published studies (including 31 AD studies, 21 bvFTD studies, 12 CBS studies and eight PNFA studies) found that bvFTD network mapping was in the anterior insula and orbitofrontal cortex, while nfvPPA network mapping was in the left inferior frontal gyrus, which is roughly consistent with the network localization in this study.30 This is also consistent with our previous understanding of the abnormal brain regions in FTD.1,2,8

Apathy-specific atrophy network mapping

Apathy can be divided into behavioural, social and emotional apathy and is often associated with symptoms such as lack of pleasure.31 Apathy is an early symptom in FTD, leading to an ideal model for research. Depression and apathy sometimes interfere with each other, thus excluding patients with depression can ensure the reliability of our apathy-specific atrophy network. The apathy-atrophy network maps were in the orbitofrontal cortex and ventral striatum, which are consistent with brain regions associated with apathy reported in previous traditional analyses. A recent study on the mechanism of apathy in bvFTD revealed a close relationship between aversion effort and the dorsomedial prefrontal cortex.32 Other neuroimaging studies have found that apathy is closely related to various brain abnormalities, such as atrophy, decreased metabolism and/or reduced perfusion. These regions include the orbitofrontal cortex, dorsal and ventral anterior cingulate cortices, medial and lateral prefrontal cortices, and posterior cingulate cortex, as well as the integrity of the uncinate fasciculus and superior longitudinal fasciculus.5,8,33-35 These areas are all situated in the anterior insula-striatum regions.

A study using the voxel-based lesion-symptom mapping method discovered a link between apathy in individuals with traumatic brain injuries and brain damage located in both limbic and cortical regions within the frontal and insula areas.36 This supports our network mapping results. Additionally, a previous case study reported improved executive function and alleviation of apathy symptoms in a bvFTD patient after receiving transcranial direct current stimulation (with the anode placed between F3 and FP1, corresponding to the left dorsolateral prefrontal cortex, and the cathode placed in the right orbitofrontal region).3 Our findings provide more scientific evidence for the selection of non-invasive transcranial electrical stimulation targets.

Disinhibition-specific atrophy network mapping

In this study, we found that the disinhibition network mapping overlapped with the criminal lesion network, particularly centred on the orbitofrontal cortex and temporal cortex. Disinhibition is a unique clinical feature of FTD, mainly characterized by impulsive behaviour, excessive excitement, non-adherence to social norms and increased sexual or food desire.37 No lesion network mapping study has targeted behaviour disinhibition in neurodegenerative disease and our study was the first to elucidate it. Criminal behaviours involve some extreme behaviours that may occur less frequently in FTD patients, but most behaviours are similar.23 Therefore, the network mapping of criminal lesions lends support to the causal inference of the atrophy network mapping of disinhibition. In previous studies on FTD using single-modal neuroimaging, disinhibition in bvFTD was associated with brain regions, such as the orbital, frontal and limbic lobes.5,34,38-48 In participants with traumatic brain injuries, disinhibition is associated with brain damage in the frontal and temporal lobes, gyrus rectus and insula, which can be identified using the voxel-based lesion-symptom mapping method.49

In the multivariate regression analysis of this study, functional connectivity between brain atrophy maps and the peak location of the disinhibition atrophy network was correlated with the NPI disinhibition severity score. However, no other correlation was found between criminal lesion network and NPI disinhibition severity score/FBI disinhibition score, disinhibition atrophy network and FBI disinhibition score. This could be attributed to the limited sample size for MRI of patients exhibiting disinhibition. In addition, we found a difference of rate in disinhibition between the Xuanwu Hospital and FTLDNI (29.5% versus 67%). Potential reasons for this phenomenon may relate to cultural differences. First, in China, the fields of neurology and psychiatry are distinct from each other. Xuanwu Hospital is focused on neurology and lacks a psychiatry department. FTD patients whose symptoms are primarily characterized by behavioural disinhibition, are more likely to be seen by psychiatric departments. Second, in China, the patient with behaviour disinhibition as a main symptom find it difficult to undergo MRI examination. Guardians of FTD patients in China tend to be conservative and wary of sedatives.

Limitations

This study has several limitations. First, in the second part of analysis, the merging of the datasets increased the sample size and statistical power but also introduced heterogeneity among different cohorts. Second, in this study, regions functionally related to the brain atrophy locations of each patient were determined using a standardized connectivity template derived from normal subjects, rather than using the patients’ individual functional connectivity profile. Brain atrophy may reduce or alter the functional connectivity strength with other brain regions. While a large scale standardized connectivity group increases the confidence in our connectivity estimation, it does not account for important individual differences in functional connections. Last, the subjects in this study were included based on clinical diagnosis rather than pathological diagnosis.

Conclusion

Single-subject atrophy maps in FTD are heterogeneous and atrophy network mapping can identify a common FTD atrophy network. First, across clinical subtypes, atrophy network maps vary. bvFTD connects to frontal and temporal cortical areas, svPPA mainly impacts the anterior temporal region, and nfvPPA targets the inferior frontal gyrus and precentral cortex regions. Second, the apathy-specific atrophy network is primarily located in the orbitofrontal cortex and ventral striatum, while the disinhibition-specific network is primarily located in the orbitofrontal and temporal cortex, which provides a solid foundation of potential target for future research on neuromodulation. Brain atrophy network mapping can localize individual brain atrophy maps to clinical subtype and symptom-specific brain networks, helping to deepen the understanding of the brain-behaviour relationship.

Supplementary Material

awae067_Supplementary_Data

Acknowledgements

We gratefully acknowledge the investigators at NIFD/FTLDNI contributed to the design and implementation of FTLDNI and/or provided data but did not participate in the analysis or writing of this report (unless otherwise listed, https://4rtni-ftldni.ini.usc.edu/).

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Funding

National Natural Science Foundation of China 82271464 (L.W.).

Competing interests

The authors report no competing interests.

Supplementary material

Supplementary material is available at Brain online.
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