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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

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71440
10.1038/s41598-024-71440-0
Article
Neural basis of false recognition in Alzheimer’s disease and dementia with lewy bodies
Chadani Yoshihiro 1
Fujito Ryoko 1
Kimura Naohiro 28
Kawai Ryo 1
Kashibayashi Tetsuo 3
Takahashi Ryuichi 3
Kanemoto Hideki 47
Ishii Kazunari 5
Tagai Kenji 6
Shinagawa Shunichiro 6
Ikeda Manabu 4
Kazui Hiroaki kazui@kochi-u.ac.jp

1
1 https://ror.org/01xxp6985 grid.278276.e 0000 0001 0659 9825 Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kohasu Oko-cho, Nankoku City, Kochi, 783-8505 Japan
2 https://ror.org/01xxp6985 grid.278276.e 0000 0001 0659 9825 Graduate School of Integrated Arts and Sciences, Kochi Medical School, Kochi University, Kohasu Oko-cho, Nankoku City, Kochi, 783-8505 Japan
3 Dementia-related Disease Medical Center, Hyogo Prefectural Rehabilitation Hospital at Nishi-Harima, 1-7-1, Kouto, Shingu-cho, Tatsuno City, Hyogo, 679-5165 Japan
4 https://ror.org/035t8zc32 grid.136593.b 0000 0004 0373 3971 Department of Psychiatry, Osaka University Graduate School of Medicine, D3, 2-2 Yamadaoka, Suita City, Osaka, 565-0871 Japan
5 https://ror.org/05kt9ap64 grid.258622.9 0000 0004 1936 9967 Department of Radiology, Kindai University, Faculty of Medicine, 377-2 Ohnohigashi, Osakasayama City, Osaka, 589-8511 Japan
6 https://ror.org/039ygjf22 grid.411898.d 0000 0001 0661 2073 Department of Psychiatry, Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8471 Japan
7 https://ror.org/035t8zc32 grid.136593.b 0000 0004 0373 3971 Health and Counseling Center, Osaka University, 1-17, Machikaneyama-cho, Toyonaka, Osaka, 560-0043 Japan
8 Department of Rehabilitation, Atago Hospital Branch, 6012-1, Nagahama, Kochi City, Kochi, 781-0270 Japan
12 9 2024
12 9 2024
2024
14 2129016 4 2024
28 8 2024
© The Author(s) 2024
2024
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In Alzheimer’s disease (AD), reports on the association between false recognition and brain structure have been inconsistent. In dementia with Lewy bodies (DLB), no such association has been reported. This study aimed to identify brain regions associated with false recognition in AD and DLB by analyzing regional gray matter volume (rGMV). We included 184 patients with AD and 60 patients with DLB. The number of false recognitions was assessed using the Alzheimer’s Disease Assessment Scale’ word recognition task. Brain regions associated with the number of false recognitions were examined by voxel-based morphometry analysis. The number of false recognitions significantly negatively correlated with rGMV in the bilateral hippocampus, left parahippocampal gyrus, bilateral amygdala, and bilateral entorhinal cortex in patients with AD (p < 0.05, family-wise error [FEW] corrected) and in the bilateral hippocampus, left parahippocampal gyrus, right inferior frontal gyrus, right middle frontal gyrus, right basal forebrain, right insula, left medial and lateral orbital gyri, and left fusiform in those with DLB (p < 0.05, FWE corrected). Bilateral hippocampus and left parahippocampal gyrus were associated with false recognition in both diseases. However, we found there were regions where the association between false recognition and rGMV differed from disease to disease.

Keywords

False recognition
Alzheimer’s dementia
Dementia with Lewy bodies
Voxel-based morphometry
Statistical parametric mapping
Alzheimer’s Disease Assessment Scale
Subject terms

Neurological disorders
Neuroscience
Cognitive neuroscience
Japan Agency for Medical Research and DevelopmentJP21dk0207056 Kazui Hiroaki issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

In Alzheimer’s disease (AD), medial temporal lobe regions such as the hippocampus and entorhinal cortex are affected1, causing recent memory impairment characterized by rapid memory information loss2. Furthermore, patients with AD often not only forget memories but also remember things that did not actually happen3, a phenomenon called “false memory.” Actions based on false memories can lead to impaired daily living4. One of the manifestations of false memories is false recognition. A person manifesting false recognition misidentifies new information as previously learned information when it is encountered5.

False recognition is also often observed in recognition tasks in neuropsychological tests routinely performed on patients with memory impairment. False recall occurs less frequently in the recall task. Therefore, it is important to identify brain dysfunction associated with false recognition. According to dual-process theories of memory, recognition memory depends on recollection (an intentional use of memory) and familiarity (an automatic use of memory)6. False recognition is more likely to occur in lure items that are similar to learned items7 and is thought to result from decisions based on familiarity for the lure when memory is inadequate. Furthermore, the activation-monitoring theory7–9 and fuzzy trace theory10,11 regarding the mechanism of false recognition both assumed a failure of the retrieval monitoring process as the background of false recognition. Various other cognitive backgrounds related to false recognition have also been proposed, including failures in source memory attribution12, source monitoring13, pattern separation14, and low working memory13. Source memory (the encoding and retrieval of contextual memory information) and pattern separation (the ability to distinguish between items or episodes that are similar to memory information) are reportedly associated with the medial temporal lobe, including the hippocampus15,16. Furthermore, source monitoring function, which inhibits errors, is associated with frontal lobe function17, and working memory is reportedly associated with frontal lobe activity18.

However, the neural basis for false recognition in patients with AD remains unclear. Some magnetic resonance imaging (MRI) studies examined the association between false recognition and brain structures in patients with AD and found that false recognition negatively correlates with hippocampal formation and amygdala volume19, and with gray matter density in the frontal lobe, right temporal pole, medial temporal lobe, and thalamus20. False recognition has also been recently reported to exhibit a negative correlation with gray matter volume in the bilateral hippocampus, left entorhinal cortex, and left fusiform gyrus21. However, a longitudinal study found no significant correlation between false recognition and brain atrophy22. Thus, the results of studies on the association between false recognition and brain structure in patients with AD are inconsistent, possibly because of differences in sample size, tests used to measure false recognition, and analysis methods (whole-brain analysis or region-of-interest analysis).

Studies on false recognition in dementia with Lewy bodies (DLB) are also few. DLB is the second most common neurodegenerative dementia in the geriatric population after AD23. In a study examining cognitive function related to false recognition in patients with DLB and those with frontotemporal dementia, inhibition deficit was associated with false recognition in patients with DLB24. Additionally, a study comparing false recognitions in patients with AD and those with DLB by using a recognition task reported that the number of false recognitions was not different between these two diseases; however, in the “remember/know” decision during false recognitions, patients with AD were more likely to respond “remember” during false recognitions, whereas those with DLB were more likely to respond “know”25. This result suggests that false recognition in patients with AD is associated with failures of source memory, whereas that in patients with DLB is associated with failures to separate memory information from familiarity, and failures of monitoring. On the basis of the results of these studies, the cognitive processes underlying false recognition may differ between AD and DLB, and possibly, the neural basis associated with false recognition also differs between AD and DLB. However, the neural basis associated with false recognition in DLB remains unreported.

Therefore, this study aimed to identify the brain regions associated with false recognition in patients with AD and in those with DLB by analyzing the regional gray matter volume (rGMV) through a whole-brain analysis using statistical parametric mapping (SPM).

Methods

Setting and participants

This study was part of a large, multicenter project entitled “Japan Multicenter Study: Behavioral and Psychological symptoms Integrated Research in Dementia (J-BIRD)-Retrospective Neuroimaging part (RN),” conducted in Japan. Five specialized centers for dementia in Japan (J-BIRD centers, described in Acknowledgement) participated in this project. This retrospective observational study conformed to the national legislation and the principles of the Declaration of Helsinki. All patient information was anonymized and stored as unlinked data before analysis. This study was undertaken after obtaining approval from the ethics committees of Kochi University Hospital and Jikei University Hospital.

We used patients’ clinical and MRI data recorded between April 2015 and December 2021 in two of the J-BIRD-RN participating institutions, namely, Osaka University and Kochi University, both of which have MRI and Japanese version of Alzheimer’s Disease Assessment Scale–cognitive subscale (ADAS J-cog)26 data. At the outpatient departments of the two centers, patients were examined using routine laboratory tests and standard neuropsychological examinations by geriatric psychiatrists specializing in dementia. Their activities of daily living were also evaluated. Based on these data, clinical dementia rating scores were calculated to determine disease severity27. Furthermore, behavioral and psychological symptoms of dementia (BPSD) were evaluated using the Neuropsychiatric Inventory (NPI)28. Global cognitive function was assessed using the Mini-Mental State Examination (MMSE)29, and working memory was evaluated using a digit span task from the third30 or fourth31 edition of the Wechsler Adult Intelligence Scale (WAIS). In addition, brain MRI was performed.

This study focused on patients with AD and DLB, which are commonly occurring dementias. Diagnoses of probable AD were based on diagnostic guidelines established by the National Institute of Aging and Alzheimer’s Association workgroups32. As for probable DLB, patients were diagnosed according to the 2005 version of the international diagnostic criteria33 until July 2017 and the 2017 version34 after August 2017. We excluded patients with developmental abnormalities, serious psychiatric diseases, substance abuse, or significant neurologic antecedents, such as brain trauma, brain tumor, vascular lesions, epilepsy, and inflammatory disease. Furthermore, as the diagnosis of AD is exclusionary, patients with only one of the four core symptoms of DLB (parkinsonism, fluctuation, visual hallucination, or REM sleep behavior disorder) may still be diagnosed with probable AD according to the diagnostic criteria. To exclude patients who might be in the prodromal stage of probable DLB or have comorbid conditions, those exhibiting one of the four core symptoms of DLB were excluded from the probable AD group.

Assessment of false recognition

False recognition was assessed using the ADAS’ word recognition task. ADAS is a widely used neuropsychological test to assess the degree of cognitive impairment in AD. It consists of the following subtasks: word recall, spoken language ability, comprehension of spoken language, word-finding difficulty, commands, naming, constructional praxis, ideational praxis, orientation, word recognition, and remembering word recognition test instructions. The total score is 70, with higher scores indicating more severe cognitive impairment. The ADAS’ word recognition task is divided into two: learning trials and identification trials. The learning trials involved reading each word aloud and trying to remember it. Immediately thereafter, the identification trials were conducted, wherein participants were presented with one word from a list of 24 words consisting of learned words and distractors and asked to answer “yes/no” to the question, “Is this one of the words I showed you before?” The same procedure was performed on all 24 words. The number of “yes” responses to a distractor word indicated the number of false recognitions, whereas that to a learned word indicated the number of true recognitions. In the Japanese version of the ADAS, the word recognition task is performed thrice. However, given that one-trial versions of the ADAS have been widely used worldwide in recent years, we used the number of false recognitions on the result of the first trial for the analysis. In addition, the number of correct responses in the word recall task of ADAS was used as an index of memory, in which the participants were asked to remember 10 words and recall as many words as possible.

MRI acquisition

This study used data on three-dimensional T1-weighted MRI performed at J-BIRD-RN-participated institutions (Philips Ingenia 3.0T at Kochi University, SIGNA EXCITE HD 1.5T and SIGNA Explorer 1.5T at Osaka University). Table 1 lists each MRI scanning protocol.Table 1 Each MRI scanning protocol.

Institution	Scanner	TE (ms)	TR (ms)	TI (ms)	Flip Angle (°)	Acquisition Matrix	Voxel Size (mm)	
Kochi University	Ingenia (PHILIPS) 3-T	3.3	7.1	824	9	224 × 224 × 170	1.14 × 1.14 × 1.2	
Osaka University M1	SIGNA Excite HD (GE) 1.5-T	4.2	12.6	400	15	256 × 256 × 124	0.9 × 0.9 × 1.4	
Osaka University M2	SIGNA Explorer (GE) 1.5-T	4.2	11	400	15	256 × 256 × 124	0.9 × 0.9 × 1.4	
TE Echo Time, TR Repetition Time, TI Inversion Time, LME-GP Low-Middle-Energy General-Purpose, GE General Electric.

MRI data analysis

This study used the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm35 implemented in the (SPM 12; Wellcome Department of Cognitive Neurology, London, United Kingdom) to normalize MRI images. Briefly, the MRI images were segmented into gray matter, white matter, and cerebrospinal fluid (CSF) space according to the tissue probability maps. Using the segmented gray matter images of all participants, we created a DARTEL template. The original MRI image of each participant was transformed into stereotactic anatomical space with deformation parameters and the template created in the DARTEL deformation process of the MRI images.

Voxel-wise correlation analysis

After smoothing images with 8 mm, we used SPM12 for the correlation analysis between the number of false recognitions and rGMV in AD and DLB. We also employed the Threshold-Free Cluster Enhancement (TFCE), a statistical method developed to improve the sensitivity and specificity of the traditional cluster-extent thresholding method in neuroimaging analyses. Introduced by Smith and Nichols in 200936, the TFCE model has become a widely used method in neuroimaging studies. It enhances the sensitivity of the traditional cluster-extent thresholding method by enhancing the local maxima of the statistical map, allowing for the detection of smaller clusters that would otherwise be missed by traditional methods. It also does not require a prespecified threshold, making it a more data-driven approach to thresholding37. The association between rGMV and the number of false recognitions was examined by voxel-by-voxel correlation analysis, considering the MRI model, intracranial volumes, sex, number of true recognitions, and NPI score as covariate parameters. Intracranial volumes were considered to the covariates to exclude the influence of individual differences in brain volume. The number of true recognitions were also considered to exclude the influence of response bias and degree of memory impairment. Additionally, the NPI score was included as a covariate to exclude the influence of BPSD. The significance threshold was set to p < 0.05 corrected for family-wise error (FWE) for the correlation analysis in patients with AD and those with DLB, and the voxel extent threshold was set to 50. Brain regions were identified using the Atlas of Neuromorphometrics implemented in SPM12.

Statistical analyses

We evaluated the differences in demographic, clinical, and cognitive variables between patients with AD and those with DLB by using the Mann–Whitney U test for continuous variables and Fisher’s exact test for dichotomous variables. Spearman’s correlation analysis was conducted to examine the association of the number of false recognitions with the number of true recognitions and the number of true recalls. Statistical data were analyzed using SPSS version 28.0.1.0 (IBM SPSS statistics 28.0.1.0), with a significance level set at p < 0.05 uncorrected.

Results

Patient demographic data and clinical assessment results

The study included 184 patients with AD and 60 patients with DLB (Table 2). Figure 1 shows the selection outline for these participants. The mean age at evaluation was 76.2 ± 10.0 years in the patients with AD and 78.5 ± 5.6 years in those with DLB. The mean disease duration was 2.6 ± 2.1 years for patients with AD and 2.4 ± 2.4 years for those with DLB, with no difference between the two groups. The neuropsychological assessment data also did not differ between the two groups; most patients in both groups had mild dementia. Conversely, the proportion of sex differed between such groups, with significantly more women in patients with AD than in those with DLB (p < 0.05). In addition, patients with DLB had significantly higher total NPI scores assessing neuropsychiatric symptoms and had more severe BPSD than those with AD (p < 0.05). On the NPI subscales, delusions, hallucinations, anxiety, and apathy were significantly higher in patients with DLB than in those with AD. However, the number of false recognitions did not significantly differ between the two groups. False recognition accounted for 50.5% of patients with AD and 43.3% of those with DLB; thus, approximately half of the patients in both groups having false recognition.Table 2 Demographic data and clinical assessment results of patients with Alzheimer’s disease and dementia with Lewy bodies.

	Patients with AD (n = 184)	Patients with DLB (n = 60)	pa	
Age at evaluation (year), mean (SD)	76.2 (10.0)	78.5 (5.6)	0.374	
Disease duration (year), mean (SD)	2.6 (2.1)	2.4 (2.4)	0.115	
Sex, male (%)	58 (31.5%)	29 (48.3%)	0.021*b	
MMSE score (/30), mean (SD)	20.8 (3.8)	21.1 (5.3)	0.315	
Scaled score of digit span in WAIS (/19), mean (SD)	8.6 (3.3)	8.5 (3.3)	0.813	
Global CDR (0/0.5/1/2/3), frequency (%)	0/93(51%)/84(46%)/7(4%)/0	0/29(48%)/22(37%)/8(13%)/1(2%)	0.313	
ADAS score (/70), mean (SD)	15.4 (6.9)	16.5 (8.5)	0.697	
 number of false recognition (/12), mean (SD)	1.5 (2.4)	1.4 (2.2)	0.488	
 number of true recognition (/12), mean (SD)	7.2 (3.6)	7.4 (3.3)	0.789	
 number of true recall (/10), mean (SD)	3.1 (1.5)	2.9 (1.6)	0.599	
 word-finding difficulty (/5), mean (SD)	0.3 (0.7)	0.4 (0.8)	0.531	
 naming (/5), mean (SD)	0.2 (0.6)	0.1 (0.5)	0.568	
NPI score (/120), median (IQR)	5 (1–11)	12 (8–24)	<0.001*	
 Delusions score (/12), median (IQR)	0 (0–0.5)	1 (0–6)	<0.001*	
 Hallucinations score (/12), median (IQR)	0 (0–0)	1 (0–4)	<0.001*	
 Agitaion/Aggression score (/12), median (IQR)	0 (0–0)	0 (0–0)	0.528	
 Dysphoria score (/12), median (IQR)	0 (0–1)	0 (0–3)	0.278	
 Anxiety score (/12), median (IQR)	0 (0–0)	0 (0–4)	<0.001*	
 Euphoria score (/12), median (IQR)	0 (0–0)	0 (0–0)	0.158	
 Apathy score (/12), median (IQR)	3 (0–4)	4 (1.5–7)	<0.001*	
 Disinhibition score (/12), median (IQR)	0 (0–0)	0 (0–0)	0.984	
 Irritability score (/12), median (IQR)	0 (0–1)	0 (0–0)	0.078	
 Aberrant motor activity score (/12), median (IQR)	0 (0–0)	0 (0–0)	0.900	
Continuous variables are presented as the mean ± standard deviation or frequency and percentage. Categorical variables are presented as frequency and percentage. NPI score are presented as median ± interquartile range.

AD Alzheimer’s Dementia, DLB Dementia with Lewy Bodies, MMSE Mini-Mental State Examinations, WAIS Wechsler Adult Intelligence Scale, ADAS Alzheimer’s Disease Assessment Scale, CDR Clinical Dementia Rating, NPI Neuropsychiatric Inventory.

p-values are calculated using the Mann–Whitney U test for continuous variables and the Fisher’s exact test for categorical variables.

* Uncorrected p-values < 0.05.

a Mann–Whitney U test.

b Fisher’s exact test.

Fig. 1 Outline of the study’s participant selection.

Correlation of false recognition with true recognition and true recall

The number of false recognitions in patients with AD significantly positively correlated with the number of true recognitions (r =.489, p < 0.05; Table 3). Meanwhile, its correlation with the number of true recalls was significant and negative, but the degree was very weak (r = −.191, p < 0.05). In patients with DLB, the number of false recognitions significantly negatively correlated with the number of true recalls (r = − .311, p < 0.05), with no significant correlation with the number of true recognitions.Table 3 Correlation of false recognition with true recognition and true recall in both groups

	True recognitions	True recalls	
False recognitions in patients with AD	.489*	-.191*	
False recognitions in patients with DLB	.154	-.311*	
The correlation coefficients between the number of false recognitions and the number of true recognitions and true recalls for the AD and DLB groups are shown, respectively.

AD Alzheimer’s disease, DLB dementia with Lewy Bodies

*Uncorrected p-values < 0.05

Voxel-wise correlation analysis between rGMV and the number of false recognitions in patients with AD

The number of false recognitions significantly negatively correlated with rGMV in the bilateral hippocampus in patients with AD (p < 0.05, FWE corrected) (Table 4, Figure 2). The region where the cluster peaked at the left hippocampus included the left parahippocaml gyrus, amygdala, and entorhinal cortex. The other region where the cluster peaked at the right hippocampus included the right parahippocampal gyrus, amygdala, and entorhinal cortex. No brain regions significantly positively correlated with the number of false recognitions.Table 4 MNI coordinates of the regions showing significant correlation with false recognition in AD.

	Talairach coordinates	Combined peak-cluster-level	
Brain region	Side	x	Y	z	TFCE	PFWE-corr	KE	
Hippocampus	Lt.	−26	−20	−26	1433.27	0.002	1427	
Hippocampus	Rt.	32	−4	−21	864.74	0.010	1330	
Brain regions and MNI coordinates of peak of cluster that were significantly negatively correlated with the number of false recognitions in patients with AD are shown.

Lt Left, Rt Right, MNI Montreal Neurological Institute, TFCE Threshold-Free Cluster Enhancement, FWE Family-Wise Error, corr corrected.

Fig. 2 Regions where the regional gray matter volume negatively correlated with the number of false recognitions in Alzheimer’s disease.

SPM12 was used for the voxel-wise correlation analysis between the regional gray matter volume and the number of false recognitions. Brain regions with p < 0.05 (FWE corrected) are marked in color. Scale bar indicates the TFCE score.

Voxel-wise correlation analysis between rGMV and the number of false recognitions in patients with DLB

In patients with DLB, the number of false recognitions significantly negatively correlated with the rGMV in the right inferior frontal gyrus, right posterior insula, left medial and lateral orbital gyri, left parahippocampal gyrus, and left fusiform (p < 0.05, FWE corrected) (Table 5, Figure 3). The wide region where the cluster peaked at the right inferior frontal gyrus included the right middle frontal gyrus, right basal forebrain, and right hippocampus. Meanwhile, the region where the cluster peaked at the left parahippocampal gyrus included the left hippocampus only. No brain regions showed a significant positive correlation with the number of false recognitions.Table 5 MNI coordinates of the regions showing significant correlation with the false recognition in DLB.

Brain region	Talairach coordinates	Combined peak-cluster-level	
Side	x	Y	z	TFCE	PFWE-corr	KE	
Inferior frontal	Rt.	50	38	6	1152.51	0.020	4350	
Posterior insula	Rt.	39	−15	8	898.63	0.043	56	
Medial orbital	Lt.	−12	34	−24	1039.41	0.028	1030	
Parahippocampal	Lt.	−22	−16	−32	1026.88	0.029	754	
Fusiform	Lt.	−40	−14	−32	916.13	0.041	174	
Lateral orbital	Lt.	−36	48	−16	864.66	0.047	115	
Brain regions and MNI coordinates of peak of cluster that were significantly negatively correlated with the number of false recognitions in patients with DLB are shown.

Rt Right, MNI Montreal Neurological Institute, TFCE Threshold-Free Cluster Enhancement, FWE Family-Wise Error, corr corrected.

Fig. 3 Regions where the regional gray matter volume negatively correlated with the number of false recognitions in dementia with Lewy bodies.

SPM12 was used for the voxel-wise correlation analysis between the regional gray matter volume and the number of false recognitions. Brain regions with p-values <0.05 (FWE corrected) are marked in color. Scale bar indicates the TFCE score.

Discussion

The brain regions associated with false recognition were identified separately in patients with AD and those with DLB by analyzing the rGMV using SPM. In our correlation analysis in patients with AD, the number of false recognitions significantly and negatively correlated with the rGMV in the hippocampus, parahippocampal gyrus, entorhinal cortex, and amygdala, bilaterally. The hippocampus and parahippocampal gyrus play important roles in recognition memory38,39. In addition, the association of the bilateral hippocampus and left parahippocampal gyrus with false recognition in patients with AD is consistent with previous studies. One such study, conducted similarly to ours, examined the association between rGMV and false recognition using the ADAS in the Alzheimer’s Disease Neuroimaging Initiative cohort21. Functional MRI studies have reported the contribution of the parahippocampal gyrus to distinguishing between false and true memories40,41. The lesion in the entorhinal cortex has been associated with familiarity impairment42, and the amygdala has been linked to false recognition in patients with AD19. In addition, the results of an fMRI study examining neural activity during false memory suggest that amygdala dysfunction associated with false recognition43.

To our knowledge, this study is the first to examine the association between false recognition and rGMV in patients with DLB. In these patients, the number of false recognitions negatively correlated with rGMV in the right inferior and middle frontal prefrontal cortex, basal forebrain, insular cortices, and left medial orbital and fusiform gyri, in addition to the rGMV-associated regions in those with AD (parahippocampal gyrus and hippocampus) in this study. According to positron emission test and fMRI studies, the right prefrontal cortex is activated in recognition tasks and is responsible for retrieval and monitoring functions5, with the right side being particularly associated with monitoring functions44. Patients with basal forebrain damage demonstrate anterograde amnesia and confabulation, and those with simultaneous prefrontal cortex damage exhibit spontaneous confabulation45. In addition, patients with more false memories (include confabulation) are more likely to have lesions in the orbitofrontal cortex46. The basal forebrain and orbitofrontal gyrus are involved in the inhibition and monitoring of currently irrelevant memories47,48. Furthermore, patients with confabulation reportedly have more false recognitions49, suggesting the association between false recognition and confabulation. This previous study discuss that confabulation may occur when executive dysfunction is more severe. We assumed that impaired inhibition and monitoring were common mechanisms for both the symptoms. As for insular cortex, memory monitoring accuracy decreased as the volume of the insular cortex decreased in a study with FreeSurfer’s analysis50. Moreover, wider regions were associated with false recognition in patients with DLB compared with those in patients with AD. These brain regions of patients with DLB reportedly have a decreased gray matter volume than those of normal controls and patients with AD47,51–54. Regarding the basal forebrain, Lewy bodies rise from the brainstem to the basal forebrain region in DLB cases55. Patients with DLB exhibit lower choline acetyltransferase levels56 and more basal forebrain atrophy57,58 than those with AD.

The number of false recognitions between patients with AD and those with DLB did not significantly differ, consistent with the results of previous study25. The number of false recognitions and true recalls were negatively correlated in both groups; the degree of correlation was extremely weak in patients with AD, whereas the correlation was stronger in patients with DLB. However, the number of false recognitions in patients with AD was positively correlated with the number of true recognitions, whereas no correlation was found in those with DLB in this study. These results are consistent with previous studies suggesting that the cognitive background of false recognition differs between patients with AD and those with DLB25. Given that false recognition occurs in different cognitive backgrounds, it likely does not represent a single cognitive dysfunction but rather a phenomenon resulting from various cognitive processes.

In this study, the NPI subscale scores for delusion, hallucination, anxiety, and apathy were higher in patients with DLB than in those with AD, consistent with the results of a previous study59. Although the sample size of the DLB group in this study was not large, it likely represented a typical DLB population. A sex difference was also observed between the AD and DLB groups, but no differences were observed in other demographic data. Sex differences were corrected for by covariates, and their influence on the results of the image analysis in this study is considered to be limited.

Limitation

This study has several limitations. First, the AD and DLB diagnoses were based on clinical diagnostic criteria, and in some participants, diagnoses using CSF and neuroimaging biomarkers were not confirmed. In addition, the possibility of AD and DLB coexistence in patients was not completely ruled out, although we excluded patients with one of the four core symptoms of DLB from the AD group to exclude DLB cases as much as possible from this group. Second, the sample size was different between the AD and DLB groups, possibly causing differences in power in SPM. Therefore, although we found an association with false recognition in different regions in AD and DLB, we cannot conclude from these results that the neural bases of false recognition are different in the two diseases. Third, the neuropsychological test data were limited, and the test data did not allow examination of cognitive background related to false recognition. Therefore, our results should be reinforced by conducting studies involving homogeneous groups of participants diagnosed using biomarkers.

Conclusion

Bilateral hippocampus and left parahippocampal gyrus were associated with false recognition in both diseases. However, we found some regions where the association between false recognition and rGMV differed from disease to disease. The neural basis associated with false recognition might be different in AD and DLB.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Abbreviations

AD Alzheimer’s disease

ADAS Alzheimer’s Disease Assessment Scale

BPSD Behavioral and psychological symptoms of dementia

CDR Clinical Dementia Rating

DARTEL Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra

DLB Dementia with Lewy bodies

MMSE Mini-mental state examination

MRI Magnetic resonance imaging

NPI Neuropsychiatric Inventory

rGMV Regional gray matter volume

SPM Statistical parametric mapping

TFCE Threshold-Free Cluster Enhancement

WAIS Wechsler Adult Intelligence Scale

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71440-0.

Acknowledgements

We would like to thank ENAGO (www.enago.jp) for English language editing.

Author contributions

Y.C. and H.K. designed the study. All author supervised the data collection. Y.C, T.K, R.T, H.K, and K.T assisted in the analysis of imaging data. Y.C was responsible for the statistical design and analysis and wrote the first manuscript draft. All authors were involved in the interpretation and presentation of the data, reviewed and revised the initial draft and subsequent versions of the manuscript, and approved the submitted version.

Funding

This work was supported by MHLW Comprehensive Research on Disability Health and Welfare Program Grant Number JPMH22GC1007 and AMED under Grant Number JP21dk0207056.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This retrospective study was approved by the ethics committee of the Kochi University Medical Hospital (Kochi, Japan) and Jikei University Hospital (Tokyo, Japan) and was conducted in accordance with the Guidelines for Good Clinical Practice and the Declaration of Helsinki. Considering the retrospective observational design of this study, wherein data were collected from medical records, an opt-out approach was used. In this approach, information regarding our study was provided on our homepage, and each participant’s consent for study participation was considered to be automatically obtained unless they expressed their desire to be excluded. The exemption on informed consent and adoption of the opt-out approach were approved by the ethics committee of Kochi University Medical Hospital.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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