Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight.

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer's Disease.

Several studies have shown white matter (WM) abnormalities in Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Nonetheless, robust characterization of WM changes has been challenging due to the methodological limitations of DTI. We applied fixel-based analyses (FBA) to examine microscopic differences in fiber density (FD) and macroscopic changes in fiber cross-section (FC) in early stages of AD (N = 393, 212 females). FBA was also compared with DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). We further investigated the correlation of FBA and tensor-derived metrics with AD pathology and cognition. FBA metrics were decreased in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations in Aß-positive patients with mild cognitive impairment compared to control groups. Metrics derived from DKI, and FW-DTI showed similar alterations whereas WM degeneration detected by DTI was more widespread. Tau-PET uptake in medial temporal regions was only correlated with reduced FC mainly in the parahippocampal cingulum in Aß-positive individuals. This tau-related WM alteration was also associated with impaired memory. Despite the spatially extensive between-group differences in DTI-metrics, the link between WM and tau aggregation was only revealed using FBA metrics implying high sensitivity but low specificity of DTI-based measures in identifying subtle tau-related WM degeneration. No relationship was found between amyloid load and any diffusion-MRI measures. Our results indicate that early tau-related WM alterations in AD are due to macrostructural changes specifically captured by FBA metrics. Thus, future studies assessing the effects of AD pathology in WM tracts should consider using FBA metrics.

Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease.

Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.

Effects of current alcohol use on brain volume among older adults in the Gothenburg H70 Birth Cohort study 2014-16.

Brain gray- and white matter changes is well described in alcohol-dependent elderly subjects; however, the effect of lower levels of alcohol consumption on the brain is poorly understood. We investigated the impact of different amounts of weekly alcohol consumption on brain structure in a population-based sample of 70-year-olds living in Gothenburg, Sweden. Cross-sectional data from 676 participants from The Gothenburg H70 Birth Cohort Study 2014-16 were included. Current alcohol consumers were divided into seven groups based on self-reported weekly amounts of alcohol consumption in grams (g) (0-50 g/week, used as reference group, 51-100 g/week, 101-150 g/week, 151-200 g/week, 201-250 g/week, 251-300 g/week, and > 300 g/week). Subcortical volumes and cortical thickness were assessed on T1-weighted structural magnetic resonance images using FreeSurfer 5.3, and white matter integrity assessed on diffusion tensor images, using tract-based statistics in FSL. General linear models were carried out to estimate associations between alcohol consumption and gray- and white matter changes in the brain. Self-reported consumption above 250 g/week was associated with thinning in the bilateral superior frontal gyrus, the right precentral gyrus, and the right lateral occipital cortex, in addition to reduced fractional anisotropy (FA) and increased mean diffusivity (MD) diffusively spread in many tracts all over the brain. No changes were found in subcortical gray matter structures. These results suggest that there is a non-linear relationship between alcohol consumption and structural brain changes, in which loss of cortical thickness only occur in non-demented 70-year-olds who consume more than 250 g/week.

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.

Neurofilament light predicts worse nonmotor symptoms and depression in Parkinson's disease.

BACKGROUND: The identification of biomarkers that reflect worse progression of nonmotor symptoms (NMS) in Parkinson's disease (PD) is currently an unmet need. The main aim of this study was to investigate whether cerebrospinal fluid (CSF) and serum neurofilament light (NfL), measured at baseline or longitudinally, can be used to predict the progression of NMS in patients with PD. METHODS: Baseline and longitudinal NfL levels were measured in the CSF and serum in 392 PD patients and 184 healthy controls from the Parkinson's Progression Marker Initiative. NMS were assessed using several scales, including, but not restricted to, the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I, the Geriatric Depression Scale (GDS) and the State-Trait Anxiety Inventory (STAI). The relationship between baseline and longitudinal NfL levels with changes in NMS was assessed using linear mixed effects models (LME) in PD patients. In addition, we compared CSF and serum NfL levels between groups and assessed the relationship between NfL biomarkers with baseline NMS. Finally, to assess the specificity of our findings we ran the previous LME models using other biomarkers such as CSF amyloid-ß1-42, total tau, phosphorylated tau181 and total α-synuclein and we also ran the models in healthy controls. RESULTS: Baseline levels and longitudinal changes in serum and CSF NfL predicted worse longitudinal MDS-UPDRS-I and depression scores over time in PD (p < 0.01). This relationship remained significant only for CSF NfL when controlling for motor and cognitive status. Furthermore, longitudinal changes in serum and CSF NfL were associated with worse anxiety over time in PD patients (p < 0.05). In contrast to CSF NfL, serum NfL levels were slightly higher at baseline (p = 0.043) and showed significant longitudinal increases (p < 0.001) in PD patients compared to controls. There were no significant correlations between NfL levels (CSF or serum) with other NMS scales, baseline NMS variables, other biomarkers or in healthy controls. CONCLUSIONS: Our findings indicate that both serum and CSF NfL are associated with worse longitudinal NMS burden, particularly in relation to the progression of depression and anxiety. Serum NfL showed stronger associations with NMS suggesting it could potentially be used as a non-invasive marker of NMS progression for PD.

Increased basal forebrain volumes could prevent cognitive decline in LRRK2 Parkinson's disease.

BACKGROUND AND OBJECTIVES: It has been recently suggested that LRRK2 mutations are associated with a more benign clinical phenotype and a potentially more preserved cholinergic function in Parkinson's disease (PD). However, to our knowledge, no studies have tested whether the better clinical progression observed in LRRK2-PD patients is associated with more preserved volumes of a cholinergic brain area, the basal forebrain (BF). To address this hypothesis, here we compared BF volumes in LRRK2 carriers with and without PD with respect to idiopathic PD (iPD) patients and controls, and assessed whether they are associated with better clinical progression observed in LRRK2-PD compared to iPD. METHODS: Thirty-one symptomatic LRRK2-PD patients and 13 asymptomatic LRRK2 individuals were included from the Parkinson's Progression Markers Initiative. In addition, 31 patients with iPD and 13 healthy controls matched to the previous groups were also included. BF volumes were automatically extracted from baseline T1-weighted MRI scans using a stereotactic atlas of cholinergic nuclei. These volumes were then compared between groups and their relationship with longitudinal cognitive changes was evaluated using linear mixed effects models. Mediation analyses assessed whether BF volumes mediated differences in cognitive trajectories between groups. RESULTS: LRRK2-PD patients showed significantly higher BF volumes compared to iPD (P = 0.019) as did asymptomatic LRRK2 subjects compared to controls (P = 0.008). There were no other significant differences in cortical regions or subcortical volumes between these groups. BF volumes predicted longitudinal decline in several cognitive functions in iPD patients but not in LRRK2-PD, who did not show cognitive changes over a 4-year follow-up period. BF volumes were a significant mediator of the different cognitive trajectories between iPD and LRRK2-PD patients (95% CI 0.056-2.955). DISCUSSION: Our findings suggest that mutations in LRRK2 are associated with increased BF volumes, potentially reflecting a compensatory hypercholinergic state that could prevent cognitive decline in LRRK2-PD patients.

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis.

INTRODUCTION: High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders. METHODS: Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R. RESULTS: Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition. CONCLUSION: These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Multiplex Connectome Changes across the Alzheimer's Disease Spectrum Using Gray Matter and Amyloid Data.

The organization of the Alzheimer's disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-ß pathology (Aß-) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aß pathology (Aß+) from the Alzheimer's Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aß-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aß + patients and changes in multiplex brain hubs in both MCI Aß + and AD Aß + groups. These findings offer a new insight into the interplay between amyloid-ß pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Associations between dietary patterns and dementia-related neuroimaging markers.

BACKGROUND: The exploration of associations between dietary patterns and dementia-related neuroimaging markers can provide insights on food combinations that may impact brain integrity. METHODS: Data were derived from the Swedish Gothenburg H70 Birth Cohort Study (n = 610). Three dietary patterns were obtained using principal component analysis. Magnetic resonance imaging markers included cortical thickness, an Alzheimer's disease (AD) signature score, small vessel disease, and white matter microstructural integrity. Adjusted linear/ordinal regression analyses were performed. RESULTS: A high-protein and alcohol dietary pattern was negatively associated with cortical thickness in the whole brain (Beta: -0.011; 95% confidence interval [CI]: -0.018 to -0.003), and with an Alzheimer's disease cortical thickness signature score (Beta: -0.013; 95% CI: -0.024 to -0.001). A positive association was found between a Mediterranean-like dietary pattern and white matter microstructural integrity (Beta: 0.078; 95% CI: 0.002-0.154). No associations were found with a Western-like dietary pattern. DISCUSSION: Dietary patterns may impact brain integrity through neurodegenerative and vascular pathways. HIGHLIGHTS: Certain dietary patterns were associated with dementia-related neuroimaging markers. A Mediterranean dietary pattern was positively associated with white matter microstructure. A high-protein and alcohol pattern was negatively associated with cortical thickness.

Plasma markers predict changes in amyloid, tau, atrophy and cognition in non-demented subjects.

It is currently unclear whether plasma biomarkers can be used as independent prognostic tools to predict changes associated with early Alzheimer's disease. In this study, we sought to address this question by assessing whether plasma biomarkers can predict changes in amyloid load, tau accumulation, brain atrophy and cognition in non-demented individuals. To achieve this, plasma amyloid-ß 42/40 (Aß42/40), phosphorylated-tau181, phosphorylated-tau217 and neurofilament light were determined in 159 non-demented individuals, 123 patients with Alzheimer's disease dementia and 35 patients with a non-Alzheimer's dementia from the Swedish BioFINDER-2 study, who underwent longitudinal amyloid (18F-flutemetamol) and tau (18F-RO948) PET, structural MRI (T1-weighted) and cognitive testing. Our univariate linear mixed effect models showed there were several significant associations between the plasma biomarkers with imaging and cognitive measures. However, when all biomarkers were included in the same multivariate linear mixed effect models, we found that increased longitudinal amyloid-PET signals were independently predicted by low baseline plasma Aß42/40 (P = 0.012), whereas increased tau-PET signals, brain atrophy and worse cognition were independently predicted by high plasma phosphorylated-tau217 (P < 0.004). These biomarkers performed equally well or better than the corresponding biomarkers measured in the CSF. In addition, they showed a similar performance to binary plasma biomarker values defined using the Youden index, which can be more easily implemented in the clinic. In addition, plasma Aß42/40 and phosphorylated-tau217 did not predict longitudinal changes in patients with a non-Alzheimer's neurodegenerative disorder. In conclusion, our findings indicate that plasma Aß42/40 and phosphorylated-tau217 could be useful in clinical practice, research and drug development as prognostic markers of future Alzheimer's disease pathology.

Plasma GFAP is an early marker of amyloid-ß but not tau pathology in Alzheimer's disease.

Although recent clinical trials targeting amyloid-ß in Alzheimer's disease have shown promising results, there is increasing evidence suggesting that understanding alternative disease pathways that interact with amyloid-ß metabolism and amyloid pathology might be important to halt the clinical deterioration. In particular, there is evidence supporting a critical role of astroglial activation and astrocytosis in Alzheimer's disease. However, so far, no studies have assessed whether astrocytosis is independently related to either amyloid-ß or tau pathology in vivo. To address this question, we determined the levels of the astrocytic marker GFAP in plasma and CSF of 217 amyloid-ß-negative cognitively unimpaired individuals, 71 amyloid-ß-positive cognitively unimpaired individuals, 78 amyloid-ß-positive cognitively impaired individuals, 63 amyloid-ß-negative cognitively impaired individuals and 75 patients with a non-Alzheimer's disease neurodegenerative disorder from the Swedish BioFINDER-2 study. Participants underwent longitudinal amyloid-ß (18F-flutemetamol) and tau (18F-RO948) PET as well as cognitive testing. We found that plasma GFAP concentration was significantly increased in all amyloid-ß-positive groups compared with participants without amyloid-ß pathology (P < 0.01). In addition, there were significant associations between plasma GFAP with higher amyloid-ß-PET signal in all amyloid-ß-positive groups, but also in cognitively normal individuals with normal amyloid-ß values (P < 0.001), which remained significant after controlling for tau-PET signal. Furthermore, plasma GFAP could predict amyloid-ß-PET positivity with an area under the curve of 0.76, which was greater than the performance achieved by CSF GFAP (0.69) and other glial markers (CSF YKL-40: 0.64, soluble TREM2: 0.71). Although correlations were also observed between tau-PET and plasma GFAP, these were no longer significant after controlling for amyloid-ß-PET. In contrast to plasma GFAP, CSF GFAP concentration was significantly increased in non-Alzheimer's disease patients compared to other groups (P < 0.05) and correlated with amyloid-ß-PET only in amyloid-ß-positive cognitively impaired individuals (P = 0.005). Finally, plasma GFAP was associated with both longitudinal amyloid-ß-PET and cognitive decline, and mediated the effect of amyloid-ß-PET on tau-PET burden, suggesting that astrocytosis secondary to amyloid-ß aggregation might promote tau accumulation. Altogether, these findings indicate that plasma GFAP is an early marker associated with brain amyloid-ß pathology but not tau aggregation, even in cognitively normal individuals with a normal amyloid-ß status. This suggests that plasma GFAP should be incorporated in current hypothetical models of Alzheimer's disease pathogenesis and be used as a non-invasive and accessible tool to detect early astrocytosis secondary to amyloid-ß pathology.

Early stages of tau pathology and its associations with functional connectivity, atrophy and memory.

In Alzheimer's disease, post-mortem studies have shown that the first cortical site where neurofibrillary tangles appear is the transentorhinal region, a subregion within the medial temporal lobe that largely overlaps with Brodmann area 35, and the entorhinal cortex. Here we used tau-PET imaging to investigate the sequence of tau pathology progression within the human medial temporal lobe and across regions in the posterior-medial system. Our objective was to study how medial temporal tau is related to functional connectivity, regional atrophy, and memory performance. We included 215 amyloid-ß- cognitively unimpaired, 81 amyloid-ß+ cognitively unimpaired and 87 amyloid-ß+ individuals with mild cognitive impairment, who each underwent 18F-RO948 tau and 18F-flutemetamol amyloid PET imaging, structural T1-MRI and memory assessments as part of the Swedish BioFINDER-2 study. First, event-based modelling revealed that the entorhinal cortex and Brodmann area 35 show the earliest signs of tau accumulation followed by the anterior and posterior hippocampus, Brodmann area 36 and the parahippocampal cortex. In later stages, tau accumulation became abnormal in neocortical temporal and finally parietal brain regions. Second, in cognitively unimpaired individuals, increased tau load was related to local atrophy in the entorhinal cortex, Brodmann area 35 and the anterior hippocampus and tau load in several anterior medial temporal lobe subregions was associated with distant atrophy of the posterior hippocampus. Tau load, but not atrophy, in these regions was associated with lower memory performance. Further, tau-related reductions in functional connectivity in critical networks between the medial temporal lobe and regions in the posterior-medial system were associated with this early memory impairment. Finally, in patients with mild cognitive impairment, the association of tau load in the hippocampus with memory performance was partially mediated by posterior hippocampal atrophy. In summary, our findings highlight the progression of tau pathology across medial temporal lobe subregions and its disease stage-specific association with memory performance. While tau pathology might affect memory performance in cognitively unimpaired individuals via reduced functional connectivity in critical medial temporal lobe-cortical networks, memory impairment in mild cognitively impaired patients is associated with posterior hippocampal atrophy.

Untangling the association of amyloid-ß and tau with synaptic and axonal loss in Alzheimer's disease.

It is currently unclear how amyloid-ß and tau deposition are linked to changes in synaptic function and axonal structure over the course of Alzheimer's disease. Here, we assessed these relationships by measuring presynaptic (synaptosomal-associated protein 25, SNAP25; growth-associated protein 43, GAP43), postsynaptic (neurogranin, NRGN) and axonal (neurofilament light chain) markers in the CSF of individuals with varying levels of amyloid-ß and tau pathology based on 18F-flutemetamol PET and 18F-flortaucipir PET. In addition, we explored the relationships between synaptic and axonal markers with cognition as well as functional and anatomical brain connectivity markers derived from resting-state functional MRI and diffusion tensor imaging. We found that the presynaptic and postsynaptic markers SNAP25, GAP43 and NRGN are elevated in early Alzheimer's disease i.e. in amyloid-ß-positive individuals without evidence of tau pathology. These markers were associated with greater amyloid-ß pathology, worse memory and functional changes in the default mode network. In contrast, neurofilament light chain was abnormal in later disease stages, i.e. in individuals with both amyloid-ß and tau pathology, and correlated with more tau and worse global cognition. Altogether, these findings support the hypothesis that amyloid-ß and tau might have differential downstream effects on synaptic and axonal function in a stage-dependent manner, with amyloid-related synaptic changes occurring first, followed by tau-related axonal degeneration.

Cortical Networks Underpinning Compensation of Verbal Fluency in Normal Aging.

Elucidating compensatory mechanisms underpinning phonemic fluency (PF) may help to minimize its decline due to normal aging or neurodegenerative diseases. We investigated cortical brain networks potentially underpinning compensation of age-related differences in PF. Using graph theory, we constructed networks from measures of thickness for PF, semantic, and executive-visuospatial cortical networks. A total of 267 cognitively healthy individuals were divided into younger age (YA, 38-58 years) and older age (OA, 59-79 years) groups with low performance (LP) and high performance (HP) in PF: YA-LP, YA-HP, OA-LP, OA-HP. We found that the same pattern of reduced efficiency and increased transitivity was associated with both HP (compensation) and OA (aberrant network organization) in the PF and semantic cortical networks. When compared with the OA-LP group, the higher PF performance in the OA-HP group was associated with more segregated PF and semantic cortical networks, greater participation of frontal nodes, and stronger correlations within the PF cortical network. We conclude that more segregated cortical networks with strong involvement of frontal nodes seemed to allow older adults to maintain their high PF performance. Nodal analyses and measures of strength were helpful to disentangle compensation from the aberrant network organization associated with OA.

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.

Longitudinal degeneration of the basal forebrain predicts subsequent dementia in Parkinson's disease.

OBJECTIVES: Cholinergic dysfunction plays a prominent role in cognitive impairment in Parkinson's disease (PD). The aim of this study was to assess the relationship of baseline and longitudinal basal forebrain atrophy with cognitive decline and dementia in PD. METHODS: We included 106 non-demented PD patients, 19 PD dementia (PDD) patients and 42 controls with longitudinal structural MRI and cognitive testing. After 4.2 ± 1.8 years, 20 non-demented PD patients were diagnosed with dementia (PD-dementia converters), whereas the rest of PD patients remained non-demented (stable-PD). We compared MRI volumes of the medial septum/diagonal band (Ch1/Ch2) and nucleus basalis of Meynert (Ch4) between groups. Cox regression analyses were applied to test whether Ch1/Ch2 or Ch4 atrophy could predict future dementia and linear mixed models assessed their association with cognitive decline. RESULTS: Compared to controls, we found reduced Ch4 baseline volumes in PD-dementia converters (p = .003) and those who already had PDD (p < .001) but not in stable-PD. Over time, there was a greater loss in Ch1/Ch2 volumes in PD-dementia converters and PDD compared to the other groups (p = .004). Baseline and longitudinal Ch4 volumes were associated with cognition (p < .002) and longitudinal Ch4 atrophy predicted future dementia (p = .009). CONCLUSIONS: Atrophy of Ch4 precedes and predicts future dementia in PD and is followed by changes in Ch1/Ch2, reflecting a posterior-anterior pattern of basal forebrain atrophy. This pattern could be used to track the spread of cholinergic degeneration and identify patients at risk of developing dementia.

Spatial patterns of tau deposition are associated with amyloid, ApoE, sex, and cognitive decline in older adults.

PURPOSE: The abnormal deposition of tau begins before the onset of clinical symptoms and seems to target specific brain networks. The aim of this study is to identify the spatial patterns of tau deposition in cognitively normal older adults and assess whether they are related to amyloid-ß (Aß), APOE, sex, and longitudinal cognitive decline. METHODS: We included 114 older adults with cross-sectional flortaucipir (FTP) and Pittsburgh Compound-B PET in addition to longitudinal cognitive testing. A voxel-wise independent component analysis was applied to FTP images to identify the spatial patterns of tau deposition. We then assessed whether tau within these patterns differed by Aß status, APOE genotype, and sex. Linear mixed effects models were built to test whether tau in each component predicted cognitive decline. Finally, we ordered the spatial components based on the frequency of high tau deposition to model tau spread. RESULTS: We found 10 biologically plausible tau patterns in the whole sample. There was greater tau in medial temporal, occipital, and orbitofrontal components in Aß-positive compared with Aß-negative individuals; in the parahippocampal component in ε3ε3 compared with ε2ε3 carriers; and in temporo-parietal and anterior frontal components in women compared with men. Higher tau in temporal and frontal components predicted longitudinal cognitive decline in memory and executive functions, respectively. Tau deposition was most frequently observed in medial temporal and ventral cortical areas, followed by lateral and primary areas. CONCLUSIONS: These findings suggest that the spatial patterns of tau in asymptomatic individuals are clinically meaningful and are associated with Aß, APOE ε2ε3, sex and cognitive decline. These patterns could be used to predict the regional spread of tau and perform in vivo tau staging in older adults.

Synaptic markers of cognitive decline in neurodegenerative diseases: a proteomic approach.

See Attems and Jellinger (doi:10.1093/brain/awx360) for a scientific commentary on this article.Cognitive changes occurring throughout the pathogenesis of neurodegenerative diseases are directly linked to synaptic loss. We used in-depth proteomics to compare 32 post-mortem human brains in the prefrontal cortex of prospectively followed patients with Alzheimer's disease, Parkinson's disease with dementia, dementia with Lewy bodies and older adults without dementia. In total, we identified 10 325 proteins, 851 of which were synaptic proteins. Levels of 25 synaptic proteins were significantly altered in the various dementia groups. Significant loss of SNAP47, GAP43, SYBU (syntabulin), LRFN2, SV2C, SYT2 (synaptotagmin 2), GRIA3 and GRIA4 were further validated on a larger cohort comprised of 92 brain samples using ELISA or western blot. Cognitive impairment before death and rate of cognitive decline significantly correlated with loss of SNAP47, SYBU, LRFN2, SV2C and GRIA3 proteins. Besides differentiating Parkinson's disease dementia, dementia with Lewy bodies, and Alzheimer's disease from controls with high sensitivity and specificity, synaptic proteins also reliably discriminated Parkinson's disease dementia from Alzheimer's disease patients. Our results suggest that these particular synaptic proteins have an important predictive and discriminative molecular fingerprint in neurodegenerative diseases and could be a potential target for early disease intervention.