Brain Communications

Freezing of gait is a common and debilitating symptom in Parkinson’s disease. Although high-frequency subthalamic deep brain stimulation is an effective treatment for Parkinson’s disease, post-operative freezing of gait severity has been reported to alleviate, deteriorate or remain constant. We conducted this study to explore the optimal stimulation sites and related connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait in Parkinson’s disease. A total of 76 Parkinson’s disease patients with freezing of gait who underwent bilateral high-frequency subthalamic stimulation were retrospectively included. The volumes of tissue activated were estimated based on individual electrode reconstruction. The optimal and sour stimulation sites were calculated at coordinate/voxel/mapping level and mapped to anatomical space based on patient-specific images and stimulation settings. The structural and functional predictive connectivity networks for the change of the post-operative Freezing of Gait-Questionnaire were also identified based on normative connectomes derived from the Parkinson’s Progression Marker Initiative database. Leave-one-out cross-validation model validated the above results, and the model remained significant after including covariates. The dorsolateral two-thirds of the subthalamic nucleus was identified as the optimal stimulation site, while the ventrocentral portion of the right subthalamic nucleus and internal capsule surrounding the left central subthalamic nucleus were considered as the sour stimulation sites. Modulation of the fibre tracts connecting to the supplementary motor area, pre-supplementary motor area and pedunculopontine nucleus accounted for the alleviation of freezing of gait, whereas tracts connecting to medial and ventrolateral prefrontal cortices contributed to the deterioration of freezing of gait. The optimal/sour stimulation sites and structural/functional predictive connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait are identified and validated through sizable Parkinson’s disease patients in this study. With the growing understanding of stimulation sites and related networks, individualized deep brain stimulation treatment with directional leads will become an optimal choice for Parkinson’s disease patients with freezing of gait in the future.

Houyou Fan

Zijian Guo

Yin Jiang

Tao Xue

Zixiao Yin

Hutao Xie

Yu Diao

Tianqi Hu

Baotian Zhao

Delong Wu

Qi An

Yichen Xu

Yuan Gao

Yutong Bai

Jianguo Zhang

Optimal subthalamic stimulation sites and related networks for freezing of gait in Parkinson’s disease

Abstract In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer’s disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer’s disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer’s Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80–0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69–0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06–0.14 units of area under curve for cognitively unimpaired, and by 0.21–0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04–0.11 units of area under curve for cognitively unimpaired and 0.05–0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80–0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

Duygu Tosun

Dallas Veitch

Paul Aisen

Clifford R Jack

William J Jagust

Ronald C Petersen

Andrew J Saykin

James Bollinger

Vitaliy Ovod

Kwasi G Mawuenyega

Randall J Bateman

Leslie M Shaw

John Q Trojanowski

Kaj Blennow

Henrik Zetterberg

Michael W Weiner

Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

Abstract Data-driven machine-learning methods on neuroimaging (e.g. MRI) are of great interest for the investigation and classification of neurological diseases. However, traditional machine learning requires domain knowledge to delineate the brain regions first, followed by feature extraction from the regions. Compared with this semi-automated approach, recently developed deep learning methods have advantages since they do not require such prior knowledge; instead, deep learning methods can automatically find features that differentiate MRIs from different cohorts. In the present study, we developed a deep learning-based classification pipeline distinguishing brain MRIs of individuals with one of three types of headaches [migraine (n = 95), acute post-traumatic headache (n = 48) and persistent post-traumatic headache (n = 49)] from those of healthy controls (n = 532) and identified the brain regions that most contributed to each classification task. Our pipeline included: (i) data preprocessing; (ii) binary classification of healthy controls versus headache type using a 3D ResNet-18; and (iii) biomarker extraction from the trained 3D ResNet-18. During the classification at the second step of our pipeline, we resolved two common issues in deep learning methods, limited training data and imbalanced samples from different categories, by incorporating a large public data set and resampling among the headache cohorts. Our method achieved the following classification accuracies when tested on independent test sets: (i) migraine versus healthy controls—75% accuracy, 66.7% sensitivity and 83.3% specificity; (2) acute post-traumatic headache versus healthy controls—75% accuracy, 66.7% sensitivity and 83.3% specificity; and (3) persistent post-traumatic headache versus healthy controls—91.7% accuracy, 100% sensitivity and 83.3% specificity. The most significant biomarkers identified by the classifier for migraine were caudate, caudal anterior cingulate, superior frontal, thalamus and ventral diencephalon. For acute post-traumatic headache, lateral occipital, cuneus, lingual, pericalcarine and superior parietal regions were identified as most significant biomarkers. Finally, for persistent post-traumatic headache, the most significant biomarkers were cerebellum, middle temporal, inferior temporal, inferior parietal and superior parietal. In conclusion, our study shows that the deep learning methods can automatically detect aberrations in the brain regions associated with different headache types. It does not require any human knowledge as input which significantly reduces human effort. It uncovers the great potential of deep learning methods for classification and automatic extraction of brain imaging–based biomarkers for these headache types.

Md Mahfuzur Rahman Siddiquee

Jay Shah

Catherine Chong

Simona Nikolova

Gina Dumkrieger

Baoxin Li

Teresa Wu

Todd J Schwedt

Headache classification and automatic biomarker extraction from structural MRIs using deep learning

头痛分类与基于深度学习的结构MRI自动生物标志物提取

Abstract Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer’s disease, including APOE-ε4, APOE-ε2, and Alzheimer’s disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N=1,541) and AD-PRS (N=1,093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age=66 years, SD=9.6) and an average of 5.3 years of MRI follow-up (max=24 years). Atrophy on volumetric MRI scans was quantified in 3 ways: (1) a composite score of regions vulnerable to Alzheimer’s disease (SPARE-AD), (2) hippocampal volume, and (3) a composite score of regions indexing advanced non-Alzheimer’s disease related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from FLAIR MRI. Using linear mixed effects models, there was an APOE-ε4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ε4/ε4 carriers, followed by ε4 heterozygouts, and lowest among ε3 homozygouts and ε2/ε2 and ε2/ε3 carriers, who did not differ from one another. The negative associations of APOE-ε4 with atrophy were reduced among those with higher education (p’s&amp;lt;0.04) and younger baseline ages (p’s&amp;lt;0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (p=0.035) and the hippocampus (p=0.014), independent of APOE-ε4 status. APOE-ε2 status (ε2/ε2 and ε2/ε3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA, or the hippocampus, but was related to greater increases in white matter hyperintensities (p=0.014). Additionally, there was an APOE-ε4 x AD-PRS interaction in relation to white matter hyperintensities (p=0.038), with greater increases in white matter hyperintensities among APOE-ε4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ε4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer’s disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ε2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer’s disease influence atrophy in a regionally-specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer’s disease risk genes contribute to white matter hyperintensity formation.

Anja Soldan

Jiangxia Wang

Corinne Pettigrew

Christos Davatzikos

Guray Erus

Timothy J Hohman

Logan Dumitrescu

Murat Bilgel

Susan M Resnick

Leonardo A Rivera-Rivera

Rebecca Langhough

Sterling C Johnson

Tammie Benzinger

John C Morris

Simon M Laws

Jurgen Fripp

Colin L Masters

Marilyn S Albert

Alzheimer disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults

阿尔茨海默病遗传风险与认知未受损成年人脑萎缩和白质高信号变化的关系

Abstract Prenatal opioid exposure has been linked to adverse effects spanning multiple neurodevelopmental domains, including cognition, motor development, attention, and vision. However, the neural basis of these abnormalities is largely unknown. A total of 49 infants, including 21 opioid-exposed and 28 controls, were enrolled and underwent MRI (43 ± 6 days old) after birth, including resting state functional MRI. Edge-centric functional networks based on dynamic functional connections were constructed, and machine-learning methods were employed to identify neural features distinguishing opioid-exposed infants from unexposed controls. An accuracy of 73.6% (sensitivity 76.25% and specificity 69.33%) was achieved using 10 times 10-fold cross-validation, which substantially outperformed those obtained using conventional static functional connections (accuracy 56.9%). More importantly, we identified that prenatal opioid exposure preferentially affects inter- rather than intra-network dynamic functional connections, particularly with the visual, subcortical, and default mode networks. Consistent results at the brain regional and connection levels were also observed, where the brain regions and connections associated with visual and higher order cognitive functions played pivotal roles in distinguishing opioid-exposed infants from controls. Our findings support the clinical phenotype of infants exposed to opioids in utero and may potentially explain the higher rates of visual and emotional problems observed in this population. Finally, our findings suggested that edge-centric networks could better capture the neural differences between opioid-exposed infants and controls by abstracting the intrinsic co-fluctuation along edges, which may provide a promising tool for future studies focusing on investigating the effects of prenatal opioid exposure on neurodevelopment.

Weixiong Jiang

Stephanie L Merhar

Zhuohao Zeng

Ziliang Zhu

Weiyan Yin

Zhen Zhou

Li Wang

Lili He

Jennifer Vannest

Weili Lin

Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks

阿片暴露婴儿的神经改变通过边缘中心脑功能网络揭示

Abstract Connected speech samples elicited by a picture description task are widely used in the assessment of aphasias, but it is not clear what their interpretation should focus on. Although such samples are easy to collect, analyses of them tend to be time-consuming, inconsistently conducted, and impractical for non-specialist settings. Here, we analysed connected speech samples from patients with the three variants of primary progressive aphasia (semantic, svPPA N = 9; logopenic, lvPPA N = 9; non-fluent, nfvPPA N = 9), progressive supranuclear palsy (PSP Richardson’s syndrome N = 10), corticobasal syndrome (CBS N = 13), and age-matched healthy controls (N = 24). There were three principal aims: (i) to determine the differences in quantitative language output and psycholinguistic properties of words produced by patients and controls; (ii) to identify the neural correlates of connected speech measures; and (iii) to develop a simple clinical measurement tool. Using data-driven methods, we optimised a 15-word checklist for use with the Boston Diagnostic Aphasia Examination ‘cookie theft’ and Mini Linguistic State Examination ‘beach scene’ pictures and tested the predictive validity of outputs from Least Absolute Shrinkage and Selection Operator (LASSO) models using an independent clinical sample from a second site. The total language output was significantly reduced in patients with nfvPPA, PSP and CBS relative to those with svPPA and controls. The speech of patients with lvPPA and svPPA contained a disproportionately greater number of words of both high frequency and high semantic diversity. Results from our exploratory voxel-based morphometry analyses across the whole group revealed correlations between grey matter volume in (i) bilateral frontal lobes with overall language output, (ii) the left frontal and superior temporal regions with speech complexity, (iii) bilateral frontotemporal regions with phonology, and (iv) bilateral cingulate and subcortical regions with age of acquisition. With the 15-word checklists, the LASSO models showed excellent accuracy for within-sample k-fold classification (over 93%) and out-of-sample validation (over 90%) between patients and controls. Between the motor disorders (nfvPPA, PSP, CBS) and lexico-semantic groups (svPPA, lvPPA), the LASSO models showed excellent accuracy for within-sample k-fold classification (88% - 92%) and moderately good (59% - 74%) differentiation for out-of-sample validation. In conclusion, we propose that a simple 15-word checklist provides a suitable screening test to identify people with progressive aphasia, while further specialist assessment is needed to differentiate accurately some groups (e.g., svPPA versus lvPPA and PSP versus nfvPPA).

Shalom K Henderson

Siddharth Ramanan

Karalyn E Patterson

Peter Garrard

Nikil Patel

Katie A Peterson

Ajay Halai

Stefano F Cappa

James B Rowe

Matthew A Lambon Ralph

Lexical markers of disordered speech in primary progressive aphasia and ‘Parkinson-plus’ disorders

原发性进行性失语和“帕金森叠加”障碍中的言语障碍词汇标记

Abstract Technologies to study messenger RNA (mRNA) in post-mortem human brain samples have greatly advanced our understanding of brain pathologies. With ongoing improvements, particularly in formalin-fixed paraffin-embedded tissue, these technologies will continue to enhance our knowledge in the future. Despite various considerations for tissue and mRNA quality, such as pre-mortem health status and RNA integrity, the impact of the tissue fixation time has not been addressed in a systemic fashion yet. In this study, we employed RNAscope to assess mRNA detectability in human post-mortem brain tissue in relation to fixation time. Our results reveal a dynamic change in mRNA detection across varying fixation durations, accompanied by an increase in signal derived from the negative probe and autofluorescence background. These findings highlight the critical relevance of standardized fixation protocols for the collection of human brain tissue in order to probe mRNA abundancy to ensure reliable and comparable results.

Charlene-Annett Hurler

Sabine Liebscher

Thomas Arzberger

Sarah Jäkel

Impact of fixation duration on messenger RNA detectability in human formalin-fixed paraffin embedded brain tissue

固定时间对人类福尔马林固定石蜡包埋脑组织中信使RNA可检测性的影响

Abstract Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects motor neurons in the brain and spinal cord. Despite the crucial role of aberrant immune responses in ALS pathogenesis, studies investigating immunological profiles in the cerebrospinal fluid (CSF) of ALS patients have reported inconsistent findings. Herein, we explored the intrathecal adaptive immune response and features of circulating T cells between CSF and blood of ALS patients, using single-cell RNA and T cell receptor (TCR) sequencing. This study comprised a total of 11 patients with apparently sporadic ALS and three controls with non-inflammatory diseases. We collected CSF from all participants, and for three ALS patients, we additionally obtained paired samples of peripheral blood mononuclear cells (PBMCs). Utilizing droplet-based single-cell RNA and TCR sequencing, we analyzed immunological profiles, gene expression characteristics, and clonality. Furthermore, we examined T cell characteristics in both PBMC and CSF samples, evaluating the shared T cell clones across these compartments. In the CSF, ALS patients exhibited a lower proportion of CD4+ T cells (45.2% vs. 61.2%, p = 0.005), and a higher proportion of CD8+ GZMKhi effector memory T cells (TEMs) than controls (21.7% vs. 16.8%, p = 0.060). Higher clonality was observed in CD8+ TEMs in ALS patients compared to controls. In addition, CSF macrophages of ALS patients exhibited a significant increase in chemokines recruiting CD8+ TEMs. Immunohistochemical analysis showed slightly higher proportions of T cells in the perivascular and parenchymal spaces in ALS patients than in controls, and CD8+ TEMs co-localized with neurons or astrocytes in the motor cortices of ALS patients. Clonally expanded CD8+ GZMKhi TEMs primarily comprised shared T cell clones between CSF and PBMCs. Moreover, the shared CD8+ TEMs of PBMCs exhibited gene expression profiles similar to CSF T cells. ALS patients showed an increase in proportion and clonality of CD8+ GZMKhi TEMs and activated features of macrophages in CSF. The shared T cell clone between CSF and blood was mainly composed of expanded CD8+ GZMKhi TEMs. In conclusion, single-cell immune profiling provided novel insights into the pathogenesis of ALS, characterized by activated macrophages and clonally expanded CD8+ T cells potentially communicate between the central nervous system and peripheral circulation.

Hyo Jae Kim

Jae-Jun Ban

Junho Kang

Hye-Ryeong Im

Sun Hi Ko

Jung-Joon Sung

Sung-Hye Park

Jong-Eun Park

Seok-Jin Choi

Single-cell analysis reveals expanded CD8+ <i>GZMK</i>high T cells in CSF and shared peripheral clones in sporadic amyotrophic lateral sclerosis

单细胞分析揭示CSF中扩增的CD8+ <i>GZMK</i>high T细胞及散发性肌萎缩侧索硬化症中的共享外周克隆

Abstract Agitated depression (A-MDD) is a severe subtype of major depressive disorder, with an increased risk of suicidality and the potential to evolve into bipolar disorder. Despite its clinical significance, the neural basis remains unclear. We hypothesize that psychomotor agitation, marked by pressured speech and racing thoughts, is linked to disruptions in brain dynamics. To test this hypothesis, we examined brain dynamics using time delay estimation and edge-center time series, as well as dynamic connections between the somatomotor network (SMN) and the default mode network in 44 patients with A-MDD, 75 with non-agitated MDD (NA-MDD), and 94 healthy controls. Our results revealed that the neural co-acitvity duration was shorter in the A-MDD group compared with both the NA-MDD and controls (A-MDD vs. NA-MDD: t = 2.295; A-MDD vs. controls: t = 2.192, all p &amp;lt; 0.05). In addition, the dynamic of neural fluctuation in SMN altered in the A-MDD group than in the NA-MDD group (t = –2.616, p = 0.011) and was correlated with agitation severity (β = –0.228, p = 0.011). The inter-network connection was reduced in the A-MDD group compared with the control group (t = 2.102, p = .037), especially at low-amplitude time points (t = 2.139, p = .034). These findings indicate rapid neural fluctuations and disrupted dynamic coupling between the SMN and default mode network in A-MDD, potentially underlying the psychomotor agitation characteristic of this subtype. These insights contribute to a more nuanced understanding of the heterogeneity of depression and have implications for differential diagnosis and treatment strategies.

Qunjun Liang

Ziyun Xu

Shengli Chen

Shiwei Lin

Xiaoshan Lin

Ying Li

Yingli Zhang

Bo Peng

Gangqiang Hou

Yingwei Qiu

Temporal dysregulation of the somatomotor network in agitated depression

激越性抑郁中躯体运动网络的时间失调

Abstract Understanding the nature and onset of neurophysiological changes, and the selective vulnerability of central hub regions in the functional network, may aid in managing the growing impact of Alzheimer’s disease on society. However, the precise neurophysiological alterations occurring in the preclinical stage of human Alzheimer’s disease remain controversial. This study aims to provide increased insights on quantitative neurophysiological alterations during a true early-stage of Alzheimer’s disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional and whole-brain neurophysiological changes in a unique cohort of 11 cognitively unimpaired individuals with pathogenic mutations in the presenilin-1 or amyloid precursor protein genes and a 1:3 matched control group (n = 33) with a median age of 49 years. We examined several quantitative magnetoencephalography measures that have been shown robust in detecting differences in sporadic Alzheimer’s disease patients and are sensitive to excitation-inhibition imbalance. This includes spectral power and functional connectivity in different frequency bands. We also investigated hub vulnerability using the hub disruption index. To understand how magnetoencephalography measures change as the disease progresses through its preclinical stage, correlations between magnetoencephalography outcomes and various clinical variables like age were analyzed. A comparison of spectral power between mutation carriers and controls revealed oscillatory slowing, characterized by widespread higher theta (4–8 Hz) power, a lower posterior peak frequency, and lower occipital alpha 2 (10–13 Hz) power. Functional connectivity analyses presented a lower whole-brain (amplitude-based) functional connectivity in the alpha (8–13 Hz) and beta (13–30 Hz) bands, predominantly located in parieto-temporal hub regions. Furthermore, we found a significant hub disruption index for (phase-based) functional connectivity in the theta band, attributed to both higher functional connectivity in “non-hub” regions alongside a hub disruption. Neurophysiological changes did not correlate with indicators of preclinical disease progression in mutation carriers after multiple comparisons correction. Our findings provide evidence that oscillatory slowing and functional connectivity differences occur before cognitive impairment in individuals with autosomal dominant mutations leading to early-onset Alzheimer’s disease. The nature and direction of these alterations are comparable to those observed in the clinical stages of Alzheimer’s disease, suggest an early excitation-inhibition imbalance, and fit with the activity-dependent functional degeneration hypothesis. These insights may prove useful for early diagnosis and intervention in the future.

Anne M van Nifterick

Willem de Haan

Cornelis J Stam

Arjan Hillebrand

Philip Scheltens

Ronald E van Kesteren

Alida A Gouw

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

认知未受损的常染色体显性阿尔茨海默病中的功能网络中断：一项脑磁图研究

acBWQ6	Brain. Commun.
Journal Impact	4.12
mbqgSu	NEUROSCIENCES(Q2)

ISSN	2632-1297
h-index	34

u9zg3I	医学生物心理学神经科学内科学精神科病理疾病遗传学化学生物化学内分泌学放射科认知物理
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ak9yM6	A7IHrH: 2019，sQJ/D8: 95.92%，sE9og+:2.40%， wzlKC/: 94.24%
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JrpmBe	网友分享经验：6 Weeks

Brain Communications

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Optimal subthalamic stimulation sites and related networks for freezing of gait in Parkinson’s disease

Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

Headache classification and automatic biomarker extraction from structural MRIs using deep learning

Alzheimer disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults

Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks

Lexical markers of disordered speech in primary progressive aphasia and ‘Parkinson-plus’ disorders

Impact of fixation duration on messenger RNA detectability in human formalin-fixed paraffin embedded brain tissue

Single-cell analysis reveals expanded CD8+ GZMKhigh T cells in CSF and shared peripheral clones in sporadic amyotrophic lateral sclerosis

Temporal dysregulation of the somatomotor network in agitated depression

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study