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ObjectivesThe altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson’s disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM).MethodsIn our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model.ResultsThe hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral “swallow tail” sign region was significant for classification.ConclusionThe integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

IntroductionAlzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer’s disease, we built an Alzheimer’s segmentation and classification (AL-SCF) pipeline based on machine learning.MethodsIn our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.ResultsOur proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.DiscussionThe AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.

Rac1 is critically involved in the regulation of the actin cytoskeleton, neuronal structure, synaptic plasticity, and memory. Rac1 overactivation is reported in human patients and animal models of Alzheimer’s disease (AD) and contributes to their spatial memory deficits, but whether Rac1 dysregulation is also important in other forms of memory deficits is unknown. In addition, the cell types and synaptic mechanisms involved remain unclear. In this study, we used local injections of AAV virus containing a dominant-negative (DN) Rac1 under the control of CaMKIIα promoter and found that the reduction of Rac1 hyperactivity in ventral hippocampal excitatory neurons improves social recognition memory in APP/PS1 mice. Expression of DN Rac1 also improves long-term potentiation, a key synaptic mechanism for memory formation. Our results suggest that overactivation of Rac1 in hippocampal excitatory neurons contributes to social memory deficits in APP/PS1 mice and that manipulating Rac1 activity may provide a potential therapeutic strategy to treat social deficits in AD.

BackgroundPosterior cortical atrophy (PCA) and semantic dementia (SD) are focal syndromes involving different cerebral regions. This study aimed to demonstrate the existence of abnormal functional connectivity (FC) with an affected network in PCA and SD.MethodsA total of 10 patients with PCA, 12 patients with SD, and 11 controls were recruited to undergo a detailed clinical history interview and physical examination, neuropsychological assessments, and PET/MRI scan. Seed-based FC analyses were conducted to construct FC in language network, visual network, and salience network. The two-sample t-test was performed to reveal distinct FC patterns in PCA and SD, and we further related the FC difference to cognition. Meanwhile, the uptake value of fluorodeoxyglucose in regions with FC alteration was also extracted for comparison.ResultsWe found a global cognitive impairment in patients with PCA and SD. The results of FC analyses showed that patients with PCA present decreased FC in left precentral gyrus to left V1 and increased FC in right inferior frontal gyrus to right V1 in the visual network, right medial frontal gyrus and left fusiform to left anterior temporal lobe and post-superior temporal gyrus in the language network, and left superior temporal gyrus to left anterior insula in the salience network, which were related to cognitive function. Patients with SD had decreased FC from right superior frontal gyrus, right middle frontal gyrus and right superior frontal gyrus to left anterior temporal lobe, or post-superior temporal gyrus in the language network, as well as left superior frontal gyrus to right anterior insula in the salience network, positively relating to cognitive function, but increased FC in the right superior temporal gyrus to left anterior temporal lobe in the language network, and right insula and left anterior cingulum to right anterior insula in the salience network, negatively relating to cognitive function. Most of the regions with FC change in patients with PCA and SD had abnormal metabolism simultaneously.ConclusionAbnormal connectivity spread over the cortex involving language and salience networks was common in patients with PCA and SD, whereas FC change involving the visual network was unique to patients with PCA. The FC changes were matched for cognitive deficits.

Background and ObjectiveHearing loss (HL) is one of the modifiable risk factors for Alzheimer's disease (AD). However, the underlying mechanism behind HL in AD remains elusive. A possible mechanism is cognitive load hypothesis, which postulates that over-processing of degraded auditory signals in the auditory cortex leads to deficits in other cognitive functions. Given mild cognitive impairment (MCI) is a prodromal stage of AD, untangling the association between HL and MCI might provide insights for potential mechanism behind HL.MethodsWe included 85 cognitively normal (CN) subjects with no hearing loss (NHL), 24 CN with HL, 103 mild cognitive impairment (MCI) patients with NHL, and 23 MCI with HL from the ADNI database. All subjects underwent resting-state functional MRI and neuropsychological scale assessments. Fractional amplitude of low-frequency fluctuation (fALFF) was used to reflect spontaneous brain activity. The mixed-effects analysis was applied to explore the interactive effects between HL and cognitive status (GRF corrected, voxel p-value

Background: Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer’s disease (AD) and has a high risk of progression to AD. Cigarette smoking is one of the important modifiable risk factors in AD progression. Cholinergic dysfunction, especially the nucleus basalis of Meynert (NBM), is the converging target connecting smoking and AD. However, how cigarette smoking affects NBM connectivity in MCI remains unclear.Objective: This study aimed to evaluate the interaction effects of condition (non-smoking vs. smoking) and diagnosis [cognitively normal (CN) vs. MCI] based on the resting-state functional connectivity (rsFC) of the NBM.Methods: After propensity score matching, we included 86 non-smoking CN, 44 smoking CN, 62 non-smoking MCI, and 32 smoking MCI. All subjects underwent structural and functional magnetic resonance imaging scans and neuropsychological tests. The seed-based rsFC of the NBM with the whole-brain voxel was calculated. Furthermore, the mixed effect analysis was performed to explore the interaction effects between condition and diagnosis on rsFC of the NBM.Results: The interaction effects of condition × diagnosis on rsFC of the NBM were observed in the bilateral prefrontal cortex (PFC), bilateral supplementary motor area (SMA), and right precuneus/middle occipital gyrus (MOG). Specifically, the smoking CN showed decreased rsFC between left NBM and PFC and increased rsFC between left NBM and SMA compared with non-smoking CN and smoking MCI. The smoking MCI showed reduced rsFC between right NBM and precuneus/MOG compared with non-smoking MCI. Additionally, rsFC between the NBM and SMA showed a significant negative correlation with Wechsler Memory Scale-Logical Memory (WMS-LM) immediate recall in smoking CN (r = −0.321, p = 0.041).Conclusion: Our findings indicate that chronic nicotine exposure through smoking may lead to functional connectivity disruption between the NBM and precuneus in MCI patients. The distinct alteration patterns on NBM connectivity in CN smokers and MCI smokers suggest that cigarette smoking has different influences on normal and impaired cognition.

BackgroundLate-onset Alzheimer’s disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general “polygenic hazard score” (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology.MethodsWe collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established.ResultsWe found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum.ConclusionPHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.

Background: Anosognosia is a significant symptom in patients with mild cognitive impairment (MCI) while the underlying neurological mechanism behind it is still unclear.Methods: A total of 121 subjects were included and classified into three groups, including 39 normal controls (NCs), 42 individuals with MCI without anosognosia (MCI-NA), and 40 individuals with MCI with anosognosia (MCI-A), based on their everyday cognition (ECog) questionnaire (discrepancy score). Resting-state functional MRIs were acquired from all the subjects, and the static amplitudes of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance were investigated to evaluate the intrinsic functional network strength and stability, respectively, and both were corrected by age, sex, education, and gray matter volume. Eventually, correlation analyses were conducted to explore the relationship between brain activity changes and cognitive status in all the subjects.Results: No significant difference was found between MCI-A and MCI-NA (P > 0.05) in cognitive ability. Regarding intrinsic brain activity, MCI-A had increased sALFF and dALFF variance in the anterior cingulate cortex (ACC) relative to MCI-NA, as well as decreased sALFF and dALFF variance in the precuneus relative to MCI-NA and controls. Moreover, MCI-A had decreased sALFF in the inferior temporal gyrus (ITG) and paracentral lobule (PCL) compared to MCI-NA. Among all the subjects, correlation analyses showed that the sALFF and dALFF variance in the precuneus was related to the Ecog discrepancy score (r = 0.232 and 0.235, respectively), immediate story recall (r = 0.200 and 0.277, respectively), and delayed story recall (r = 0.255 and 0.298, respectively).Conclusion: Alterations of intrinsic brain activation in the ACC and precuneus seem to be associated with the anosognosia symptom in patients with MCI.

Background: Before the apparent cognitive decline, subjects on the course of Alzheimer's disease (AD) can have significantly altered spontaneous brain activity, which could be potentially used for early diagnosis. As previous studies investigating local brain activity may suffer from the problem of cortical signal aliasing during volume-based analysis, we aimed to investigate the cortical functional alterations in the AD continuum using a surface-based approach.Methods: Based on biomarker profile “A/T,” we included 11 healthy controls (HC, A–T–), 22 preclinical AD (CU, A+T+), 33 prodromal AD (MCI, A+T+), and 20 AD with dementia (d-AD, A+T+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate the changes of spontaneous brain activity, which was performed in the classic frequency band (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz) band, and slow-5 (0.01–0.027 Hz) band.Results: Under classic frequency band and slow-4 band, analysis of covariance (ANCOVA) showed that there were significant differences of standardized ALFF (zALFF) in the left posterior cingulate cortex (PCC) among the four groups. The post-hoc analyses showed that under the classic frequency band, the AD group had significantly decreased zALFF compared with the other three groups, and the cognitively unimpaired (CU) group had decreased zALFF compared with the healthy control (HC) group. Under the slow-4 band, more group differences were detected (HC > CU/MCI > d-AD). The accuracy of classifying CU, mild cognitive impairment (MCI), and AD from HC by left PCC activity under the slow-4 band were 0.774, 0.744, and 0.920, respectively. Moreover, the zALFF values of the left PCC had significant correlations with cerebrospinal fluid (CSF) biomarkers and neuropsychological tests.Conclusions: Spontaneous brain activity in the left PCC may decrease in preclinical AD when cognitive functions were relatively normal. The combination of a surfaced-based approach and specific frequency band analysis may increase sensitivity for the identification of preclinical AD subjects.

Assessing glymphatic function using in-vivo imaging method is of great value for understanding its contribution to major brain diseases. In the present study, we aim to validate the association between a variety of risk factors and a potential index of glymphatic function—Diffusion Tensor Image Analysis Along the Perivascular Space (ALPS index). We enrolled 142 subjects from communities and performed multi-modality magnetic resonance imaging scans. The ALPS index was calculated from diffusion tensor imaging data, and its associations with demographic factors, vascular factors were investigated using regression analyses. We found that the ALPS index was negatively associated with age (β = −0.284, p < 0.001). Compared to males, females had significantly higher ALPS index (β = −0.243, p = 0.001). Hypertensive subjects had significantly lower ALPS index compared to non-hypertensive subjects (β = −0.189, p = 0.013). Furthermore, venous disruption could decrease ALPS index (β = −0.215, p = 0.003). In general, our results are in consistent with previous conceptions and results from animal studies about the pathophysiology of glymphatic dysfunction. Future studies utilizing this method should consider introducing the above-mentioned factors as important covariates.

Lenticulostriate arteries (LSAs) supply blood to important subcortical areas and are, therefore, essential for maintaining the optimal functioning of the brain’s most metabolically active nuclei. Past studies have demonstrated the potential for quantifying the morphology of LSAs as biomarkers of vascular fragility or underlying arteriopathies. Thus, the current study aims to evaluate the morphological features of LSAs, their potential value in cerebrovascular risk stratification, and their concordance with other vascular risk factors in community-dwelling elderly people. A total of 125 community-dwelling elderly subjects who underwent a brain MRI scan were selected from our prospectively collected imaging database. The morphological measures of LSAs were calculated on the vascular skeletons obtained by manual tracing, and the number of LSAs was counted. Additionally, imaging biomarkers of small vessel disease were evaluated, and the diameters of major cerebral arteries were measured. The effects of vascular risk factors on LSA morphometry, as well as the relationship between LSA measures and other imaging biomarkers, were investigated. We found that smokers had shorter (p = 0.04) and straighter LSAs (p < 0.01) compared to nonsmokers, and the presence of hypertension is associated with less tortuous LSAs (p = 0.03) in community-dwelling elderly. Moreover, the middle cerebral artery diameter was positively correlated with LSA count (r = 0.278, p = 0.025) and vessel tortuosity (r = 0.257, p = 0.04). The posterior cerebral artery diameter was positively correlated with vessel tortuosity and vessel length. Considering the scarcity of noninvasive methods for measuring small artery abnormalities in the brain, the LSA morphological measures may provide valuable information to better understand cerebral small vessel degeneration during aging.

BackgroundApolipoprotein E (APOE) ε2 is a protective genetic factor for Alzheimer’s disease (AD). However, the potential interaction effects between the APOE ε2 allele and disease status on the intrinsic brain activity remain elusive.MethodsWe identified 73 healthy control (HC) with APOE ε3/ε3, 61 mild cognitive impairment (MCI) subjects with APOE ε3/ε3, 24 HC with APOE ε2/ε3, and 10 MCI subjects with APOE ε2/ε3 from the ADNI database. All subjects underwent a resting-state functional MRI and Fluoro-deoxy-glucose positron emission tomography (FDG-PET). We used a fractional amplitude of low-frequency fluctuation (fALFF) to explore the spontaneous brain activity. Based on the mixed-effects analysis, we explored the interaction effects between the APOE ε2 allele versus disease status on brain activity and metabolism in a voxel-wise fashion (GRF corrected, p < 0.01), followed by post hoc two-sample t-tests (Bonferroni corrected, p < 0.05). We then investigated the relationship between the mean imaging metrics and cognitive abilities.ResultsThere are no significant differences in gender, age, or education among the four groups. The interaction effect on brain activity was located in the inferior parietal lobule (IPL). Post hoc analysis showed that APOE ε2/ε3 MCI had an increased IPL fALFF than APOE ε3/ε3 MCI. Regarding the APOE ε2 allele effects, we found that ε2 carriers had a decreased fALFF in the transverse temporal gyrus than non-carriers. Also, FDG-PET results showed a lower SUVR of the frontal lobe in APOE ε2 carriers than non-carriers. Furthermore, fALFF of IPL was correlated with the visuospatial function (r = −0.16, p < 0.05).ConclusionAPOE ε2 carriers might have a better brain reservation when coping with AD-related pathologies.

Background: The dilation of perivascular space (PVS) has been widely used to reflect brain degeneration in clinical brain imaging studies. However, PVS characteristics exhibit large differences in healthy subjects. Such variations need to be better addressed before PVS can be used to reflect pathological changes. In the present study, we aim to investigate the potential influence of several related factors on PVS dilation in healthy elderly subjects.Methods: One-hundred and three subjects (mean age = 59.5) were retrospectively included from a prospectively collected community cohort. Multi-modal high-resolution magnetic resonance imaging and cognitive assessments were performed on each subject. Machine-learning based segmentation methods were employed to quantify PVS volume and white matter hyperintensity (WMH) volume. Multiple regression analysis was performed to reveal the influence of demographic factors, vascular risk factors, intracranial volume (ICV), major brain artery diameters, and brain atrophy on PVS dilation.Results: Multiple regression analysis showed that age was positively associated with the basal ganglia (BG) (standardized beta = 0.227, p = 0.027) and deep white matter (standardized beta = 0.220, p = 0.029) PVS volume. Hypertension was positively associated with deep white matter PVS volume (standardized beta = 0.234, p = 0.017). Furthermore, we found that ICV was strongly associated with the deep white matter PVS volume (standardized beta = 0.354, p < 0.001) while the intracranial artery diameter was negatively associated with the deep white matter PVS volume (standardized beta = −0.213, p = 0.032).Conclusions: Intracranial volume has significant influence on deep white matter PVS volume. Future studies on PVS dilation should include ICV as an important covariate.

BackgroundSmoking is a modifiable risk factor for Alzheimer’s disease (AD). However, smoking-related effects on intrinsic brain activity in high-risk AD population are still unclear.ObjectiveWe aimed to explore differences in smoking effects on brain function between healthy elderly and amnestic mild cognitive impairment (aMCI) patients using ReHo mapping.MethodsWe identified 64 healthy elderly controls and 116 aMCI patients, including 98 non-smoking and 18 smoking aMCI. Each subject underwent structural and resting-state functional MRI scanning and neuropsychological evaluations. Regional homogeneity (ReHo) mapping was used to assess regional brain synchronization. After correction for age, gender, education, and gray matter volume, we explored the difference of ReHo among groups in a voxel-wise way based on analysis of covariance (ANCOVA), followed by post hoc two-sample analyses (p < 0.05, corrected). Further, we correlated the mean ReHo with neuropsychological scales.ResultsThree groups were well-matched in age, gender, and education. Significant ReHo differences were found among three groups, located in the left supramarginal gyrus (SMG) and left angular gyrus (AG). Specifically, non-smoking aMCI had lower ReHo in SMG and AG than smoking aMCI and controls. By contrast, smoking aMCI had greater AG ReHo than healthy controls (p < 0.05). Across groups, correlation analyses showed that left AG ReHo correlated with MMSE (r = 0.18, p = 0.015), clock drawing test (r = 0.20, p = 0.007), immediate recall (r = 0.36, p < 0.001), delayed recall (r = 0.34, p < 0.001), and auditory verbal learning test (r = 0.20, p = 0.007).ConclusionSmoking might pose compensatory or protective effects on intrinsic brain activity in aMCI patients.

Background: Suspected non-Alzheimer disease pathophysiology (SNAP) refers to the subjects who feature negative β-amyloid (Aβ) but positive tau or neurodegeneration biomarkers. It accounts for a quarter of the elderly population and is associated with cognitive decline. However, the underlying pathophysiology is still unclear.Methods: We included 111 non-demented subjects, then classified them into three groups using cerebrospinal fluid (CSF) Aβ 1–42 (A), phosphorylated tau 181 (T), and total tau (N). Specifically, we identified the normal control (NC; subjects with normal biomarkers, A-T-N-), SNAP (subjects with normal amyloid but abnormal tau, A−T+), and predementia Alzheimer’s disease (AD; subjects with abnormal amyloid and tau, A+T+). Then, we used the static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance to reflect the intrinsic functional network strength and stability, respectively. Further, we performed a correlation analysis to explore the possible relationship between intrinsic brain activity changes and cognition.Results: SNAP showed decreased sALFF in left superior frontal gyrus (SFG) while increased sALFF in left insula as compared to NC. Regarding the dynamic metric, SNAP showed a similarly decreased dALFF in the left SFG and left paracentral lobule as compared to NC. By contrast, when compared to NC, predementia AD showed decreased sALFF in left inferior parietal gyrus (IPG) and right precuneus, while increased sALFF in the left insula, with more widely distributed decreased dALFF variance across the frontal, parietal and occipital lobe. When directly compared to SNAP, predementia AD showed decreased sALFF in left middle occipital gyrus and IPG, while showing decreased dALFF variance in the left temporal pole. Further correlation analysis showed that increased sALFF in the insula had a negative correlation with the general cognition in the SNAP group. Besides, sALFF and dALFF variance in the right precuneus negatively correlated with attention in the predementia AD group.Conclusion: SNAP and predementia AD show distinct functional impairment patterns. Specifically, SNAP has functional impairments that are confined to the frontal region, which is usually spared in early-stage AD, while predementia AD exhibits widely distributed functional damage involving the frontal, parietal and occipital cortex.

Recent small subcortical infarcts (RSSIs) can occur in different brain regions. Distinct etiologies might be involved for RSSIs in different locations and could further affect RSSI cavitation and functional outcomes. In this study, we aim to analyze the baseline clinical and imaging characteristics associated with the occurrence and cavitation of RSSIs in different locations. We retrospectively include patients who presented with RSSIs from a database for cerebral small vessel disease. Detailed information, including demographic, clinical, laboratory, and radiological data, were collected. We identify baseline RSSIs on diffusion-weighted images and divide them into brainstem, subcortical white matter, and basal ganglia region groups. Cavitation is evaluated on follow-up T2 fluid-attenuated inversion recovery (FLAIR) images. Statistical analysis is performed to determine factors associated with the occurrence and cavitation of RSSIs in different locations. We find that patients with brainstem RSSIs have a higher proportion of diabetes (64.1%) compared to patients with subcortical white matter (27.3%, P < 0.001) and basal ganglia region RSSIs (35.2%, P = 0.006) and have higher levels of HbA1c (7.20%) compared to patients with subcortical white matter (6.10%, P = 0.001) and basal ganglia region RSSIs (6.20%, P = 0.003). In addition, patients with brainstem RSSIs have higher NIHSS scores than patients with subcortical white matter RSSIs (2 vs 0, P = 0.001). Patients with subcortical white matter RSSIs have higher a white matter hyperintensity (WMH) burden compared to patients with basal ganglia region RSSIs (21.64 cm3 vs 11.10 cm3, P = 0.004). Follow-up analysis demonstrates that basal ganglia region RSSIs are less likely to cavitate than subcortical white matter RSSIs (61.4% vs 83.6%, P = 0.010), and contacting with WMH is associated with the cavitation of subcortical white matter RSSIs (OR: 101.760, P = 0.003). Our study demonstrates that RSSIs in different locations are associated with different clinical and imaging characteristics. Furthermore, cavitation of RSSIs might be affected by local lesion features and the surrounding environment rather than general demographic and clinical factors.

Background: The National Institute on Aging-Alzheimer’s Association (NIA-AA) has proposed a biological definition of Alzheimer’s disease (AD): individuals with both abnormal amyloid and tau biomarkers (A+T+) would be defined as AD. It remains unclear why different cognitive status is present in subjects with biological AD. Resting-state functional magnetic resonance imaging (rsfMRI) has provided an opportunity to reveal the brain activity patterns in a biologically-defined AD cohort. Accordingly, we aimed to investigate distinct brain activity patterns in subjects with existed AD pathology but in the different cognitive stages.Method: We selected individuals with AD pathology (A+T+) and healthy controls (HC, A−T−) based on the cerebrospinal fluid (CSF) biomarkers. According to the cognitive stage, we divided the A+T+ cohort into three groups: (1) preclinical AD; (2) prodromal AD; and (3) AD with dementia (d-AD). We compared spontaneous brain activity measured by a fractional amplitude of low-frequency fluctuation (fALFF) approach among four groups.Results: The analysis of covariance (ANCOVA) results showed significant differences in fALFF in the posterior cingulate cortex/precuneus (PCC/PCu). Further, compared to HC, we found increased fALFF values in the right inferior frontal gyrus (IFG) in the preclinical AD stage, whereas prodromal AD patients showed reduced fALFF in the bilateral precuneus, right middle frontal gyrus (MFG), right precentral gyrus, and postcentral gyrus. Within the d-AD group, both hyperactivity (right fusiform gyrus, right parahippocampal gyrus (PHG)/hippocampus, and inferior temporal gyrus) and hypoactivity (bilateral precuneus, left posterior cingulate cortex, left cuneus and superior occipital gyrus) were detected.Conclusion: We found the distinct brain activity patterns in different cognitive stages among the subjects defined as AD biologically. Our findings may be helpful in understanding mechanisms leading to cognitive changes in the AD pathophysiological process.

Background: Early-onset Alzheimer’s disease (EOAD) presents a different clinical profile than late-onset Alzheimer’s disease (LOAD). Neuroimaging studies have demonstrated that patients with EOAD present more atrophy and functional disconnection than LOAD patients. However, it remains unknown whether the interhemispheric functional disconnection or its underlying structural impairment contributes to the different clinical profiles of EOAD and LOAD.Methods: According to the arbitrary cut-off age of 65, we included 22 EOAD patients, 27 LOAD patients and 38 healthy controls (further divided into 21 relatively young and 17 old controls). Participants underwent resting-state functional MRI, diffusion tensor imaging (DTI) and comprehensive neuropsychological assessments. We used voxel-mirrored homotopic connectivity (VMHC) to examine interhemispheric functional connectivity. Then, we calculated the diffusion index based on tract-based spatial statistics (TBSS). Two-sample t-tests were used to assess the interhemispheric connectivity differences between each patient group and its corresponding control group.Results: We found that the EOAD patients had lower VMHC in the hippocampus, parahippocampal gyrus (PHG), superior temporal gyrus (STG) and inferior parietal cortex (IPC) than did controls. Consistently, the EOAD patients exhibited white matter (WM) tract impairment in the posterior regions. On the other hand, the LOAD patients displayed increased VMHC and impaired WM tracts in the frontal region. Correlation analyses showed that VMHC in the IPC was related to executive function in the EOAD patients (r = −0.67, P < 0.05).Conclusion: In contrast to the LOAD patients, patients with EOAD exhibited more widely disrupted interhemispheric functional and structural connectivity, which overlapped well across brain regions. In addition, for the EOAD patients, decreased interhemispheric connectivity related to executive deficits. Our study suggested that different interhemispheric connectivity damage patterns may contribute to the distinct clinical profiles in EOAD and LOAD.

Background: Amnestic mild cognitive impairment (aMCI) is a heterogeneous condition. Based on clinical symptoms, aMCI could be categorized into single-domain aMCI (SD-aMCI, only memory deficit) and multi-domain aMCI (MD-aMCI, one or more cognitive domain deficit). As core intrinsic functional architecture, inter-hemispheric connectivity maintains many cognitive abilities. However, few studies investigated whether SD-aMCI and MD-aMCI have different inter-hemispheric connectivity pattern.Methods: We evaluated inter-hemispheric connection pattern using fluorine-18 positron emission tomography – fluorodeoxyglucose (18F PET-FDG), resting-state functional MRI and structural T1 in 49 controls, 32 SD-aMCI, and 32 MD-aMCI patients. Specifically, we analyzed the 18F PET-FDG (intensity normalized by cerebellar vermis) in a voxel-wise manner. Then, we estimated inter-hemispheric functional and structural connectivity by calculating the voxel-mirrored homotopic connectivity (VMHC) and corpus callosum (CC) subregions volume. Further, we correlated inter-hemispheric indices with the behavioral score and pathological biomarkers.Results: We found that MD-aMCI exhibited more several inter-hemispheric connectivity damages than SD-aMCI. Specifically, MD-aMCI displayed hypometabolism in the bilateral middle temporal gyrus (MTG), inferior parietal lobe, and left precuneus (PCu) (p < 0.001, corrected). Correspondingly, MD-aMCI showed decreased VMHC in MTG, PCu, calcarine gyrus, and postcentral gyrus, as well as smaller mid-posterior CC than the SD-aMCI and controls (p < 0.05, corrected). Contrary to MD-aMCI, there were no neuroimaging indices with significant differences between SD-aMCI and controls, except reduced hypometabolism in bilateral MTG. Within aMCI patients, hypometabolism and reduced inter-hemispheric connectivity correlated with worse executive ability. Moreover, hypometabolism indices correlated to increased amyloid deposition.Conclusion: In conclusion, patients with MD-aMCI exhibited the more severe deficit in inter-hemispheric communication than SD-aMCI. This long-range connectivity deficit may contribute to cognitive profiles and potentially serve as a biomarker to estimate disease progression of aMCI patients.