Gray Matter Connectivity Research Articles

Differences in Grey Matter Concentrations and Functional Connectivity between Young Carriers and Non-Carriers of the APOE ε4 Genotype.

Background: The pathophysiology of Alzheimer's disease (AD) may begin developing years or even decades prior to the manifestation of its first symptoms. The APOE ε4 genotype is a prominent genetic risk for AD that has been found to be associated with brain changes across the lifespan since early adulthood. Thus, studying brain changes that may occur in young adults with an APOE ε4 status is highly relevant. Objective: Examine potential differences in grey matter (GM) and functional connectivity (FC) in brains of cognitively healthy young APOE ε4 carriers and non-carriers, denoted here as ε4(+) and ε4(-), respectively. Methods: Three Tesla magnetic resonance imaging (MRI) brain scans were acquired from cognitively healthy young participants aged approximately 20 years (n = 151). Voxel-based morphometry (VBM) analysis was employed to identify potential structural differences in GM between ε4(+) and ε4(-). In a subsequent seed-based connectivity (SBC) analysis, brain regions that structurally differed in the VBM analysis were considered as seeds and correlated with all the remaining voxels across the brains to then measure the differences in FC between groups. Results: The VBM analysis suggested that ε4(+) (n = 28) had greater GM densities relative to ε4(-) (n = 123) in the left hippocampus and the left posterior insula (puncorr < 0.001). However, the effect did not survive the correction for multiple comparisons, suggesting minimal structural differences in this age range. In contrast, the SBC analysis indicated that ε4(+) exhibited significantly decreased FC between the left hippocampus and areas of the left middle temporal gyrus (n = 27) compared to ε4(-) (n = 102). These results remained significant after multiple comparisons (pFDR < 0.05). Lastly, no statistically significant differences in FC between groups were observed for the left insular seed (pFDR > 0.05). Discussion: These results suggest early structural and functional brain changes associated with the APOE ε4 genotype on young adults. Yet, they must be cautiously interpreted and contrasted with both older adults with genetic risk for AD and patients diagnosed with AD.

Educational disparities in brain health and dementia across Latin America and the United States.

Education influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown. A total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type. Education influenced brain measures, explaining 24%-98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions. The results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research. Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.

The associations of socioeconomic position with structural brain damage and connectivity and cognitive functioning: The Maastricht Study

BackgroundSocioeconomic inequalities in cognitive impairment may partly act through structural brain damage and reduced connectivity. This study investigated the extent to which the association of early-life socioeconomic position (SEP) with later-life cognitive functioning is mediated by later-life SEP, and whether the associations of SEP with later-life cognitive functioning can be explained by structural brain damage and connectivity. MethodsWe used cross-sectional data from the Dutch population-based Maastricht Study (n = 4,839; mean age 59.2 ± 8.7 years, 49.8% women). Early-life SEP was assessed by self-reported poverty during childhood and parental education. Later-life SEP included education, occupation, and current household income. Participants underwent cognitive testing and 3-T magnetic resonance imaging to measure volumes of white matter hyperintensities, grey matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SEP, markers of structural brain damage and connectivity, and cognitive functioning. Mediation was tested using structural equation modeling. ResultsAlthough there were direct associations between both indicators of SEP and later-life cognitive functioning, a large part of the association between early-life SEP and later-life cognitive functioning was explained by later-life SEP (72.2%). The extent to which structural brain damage or connectivity acted as mediators between SEP and cognitive functioning was small (up to 5.9%). ConclusionsWe observed substantial SEP differences in later-life cognitive functioning. Associations of structural brain damage and connectivity with cognitive functioning were relatively small, and only marginally explained the SEP gradients in cognitive functioning.

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival.

Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

THE ASSOCIATIONS OF SOCIOECONOMIC POSITION WITH STRUCTURAL BRAIN DAMAGE AND CONNECTIVITY AND COGNITION

Abstract Socioeconomic inequalities in the risk for cognitive impairment have been reported, which might partly act through structural brain damage and connectivity. This study investigated the extent to which the association of early-life socioeconomic position (SEP) with later-life cognitive performance is mediated by later-life SEP, and whether the association of SEP with later-life cognitive performance can be explained by structural brain damage and connectivity. We used cross-sectional data from the population-based Maastricht study (n=4,839; mean age 59.2±8.7 years, 49.8% women). Early-life SEP was assessed retrospectively by self-reported poverty and parental education. Later-life SEP included education, occupation, and household income. Participants underwent cognitive testing and 3 T magnetic resonance imaging to measure volumes of white matter hyperintensities, gray matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SEP, MRI brain markers and cognition. Structural equation modeling tested mediation. Results indicated that higher SEP was associated with higher grey matter volume, lower white matter volume, and higher structural connectivity. Structural brain damage and lower structural connectivity were associated with lower cognition. Both higher early-life and later-life SEP were associated with higher cognition. The association of early-life SEP with later-life cognition was partly mediated by later-life SES (73.8%). However, the extent to which the association of SEP with cognition could be explained by structural brain damage or connectivity was marginal (up to 5.9%). More research is needed to investigate alternative pathways that can explain the associations between SEP with later-life cognition.

Microstructural integrity and structural connectivity of medial temporal lobe subregions is related to Alzheimer’s pathology

AbstractBackgroundThe earliest effects of Alzheimer’s disease pathology on medial temporal lobe (MTL) gray matter microstructure and white matter connectivity are still unknown. Here, we applied ultra‐high‐resolution diffusion MRI to characterize microstructural integrity and structural connectivity of the MTL, and tested associations with Alzheimer’s pathology and memory performance in cognitively normal older adults.Method83 participants (M = 71.7yrs, 66% female) received structural and ultra‐high‐resolution diffusion MRI (0.67×0.67×3mm). Subsamples received [18F]‐florbetapir (FBP) to measure Aß (n = 77; global summary SUVR), [18F]‐MK6240 to measure tau (n = 23; entorhinal SUVR), and performed an object mnemonic discrimination task (n = 74). Structural MRI was processed with ASHS to obtain MTL ROIs (Fig1). Diffusion MRI was processed with FSL DTIfit to calculate mean diffusivity (MD), an index of cellular integrity. Tractography was performed with DSI Studio. ROI‐to‐ROI streamlines were calculated (Fig3A) and used for structural connectivity graph analyses of the MTL network. Analyses controlled for age and sex, with scan interval and education in relevant models.ResultEntorhinal MK6240 SUVR was associated with increased MD in transentorhinal cortex (BA35; r = 0.72, p&lt;0.001), entorhinal cortex (EC; r = 0.61, p = 0.004), and dentate gyrus (DG; r = 0.46, p = 0.04; Fig2A), while global FBP SUVR was associated with higher MD in EC (r = 0.25, p = 0.03). Worse mnemonic discrimination performance was associated with increased MD in DG (r = ‐0.29, p = 0.01), with trends in EC (r = ‐0.23, p = 0.06) and CA3 (r = ‐0.20, p = 0.09; Fig2B). The number of streamlines between EC and DG was not significantly associated with entorhinal MK6240 SUVR (r = ‐0.32, p = 0.17), FBP SUVR, or mnemonic discrimination (ps&gt;0.37). Lower global efficiency of the MTL network was associated with increased entorhinal MK6240 SUVR (r = ‐0.50, p = 0.025) yet better mnemonic discrimination performance (r = ‐0.28, p = 0.02; Fig3B).ConclusionEntorhinal tau pathology and memory performance are closely related to microstructural integrity and structural connectivity of the MTL, suggesting tau‐related MTL neurodegeneration may drive early memory decline.

The associations of socioeconomic status with structural brain damage and connectivity and cognition: The Maastricht Study

AbstractBackgroundSocioeconomic inequalities in the risk for cognitive impairment have been reported, which might partly act through structural brain damage or differential numbers of brain connections (structural connectivity). This study investigated the extent to which the association of early‐life socioeconomic status (SES) with later‐life cognitive performance is mediated by later‐life SES, and whether the association of SES with later‐life cognitive performance can be explained by structural brain damage and connectivity.MethodWe used cross‐sectional data from the population‐based Maastricht study (n = 4,839; mean age 59.2±8.7 years, 49.8% women). Early‐life SES was assessed retrospectively by self‐reported poverty and parental education. Later‐life SES included education, occupation, and household income. Participants underwent cognitive testing and 3 T magnetic resonance imaging to measure volumes of white matter hyperintensities, gray matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SES, brain markers and cognition. Analyses were adjusted for age, sex, and type 2 diabetes (because of oversampling). Structural equation modeling tested mediation.ResultHigher SES was associated with higher grey matter volume, lower white matter volume, and higher structural connectivity. Structural brain damage and lower structural connectivity were associated with lower overall cognitive performance. Both higher early‐life and later‐life SES were associated with overall higher cognitive performance (B [95% CI] medium versus low early‐life SES = 0.17[0.12;0.23]; B high versus low early‐life SES = 0.26[0.20;0.32]; B medium versus low later‐life SES = 0.46[0.41;0.52]; B high versus low later‐life SES = 0.72[0.67;0.78]). The association of early‐life SES with later‐life cognitive performance was partly mediated by later‐life SES (73.8%). The extent to which the association of SES with cognitive performance could be explained by structural brain damage or connectivity was marginal (up to 5.9%).ConclusionThough there was an association between early‐life SES and later‐life cognitive performance independent of later‐life SES, a large part of this association could be explained by later‐life SES. Structural brain damage and connectivity marginally explained socioeconomic inequalities in cognitive performance. More research is needed to investigate alternative pathways that explain associations of life‐course SES with later‐life cognition.

Microstructural integrity and structural connectivity of medial temporal lobe subregions is related to Alzheimer’s pathology

AbstractBackgroundThe earliest effects of Alzheimer’s disease pathology on medial temporal lobe (MTL) gray matter microstructure and white matter connectivity are still unknown. Here, we applied ultra‐high‐resolution diffusion MRI to characterize microstructural integrity and structural connectivity of the MTL, and tested associations with Alzheimer’s pathology and memory performance in cognitively normal older adults.Method83 participants (M = 71.7yrs, 66% female) received structural and ultra‐high‐resolution diffusion MRI (0.67×0.67×3mm). Subsamples received [18F]‐florbetapir (FBP) to measure Aβ (n = 77; global summary SUVR), [18F]‐MK6240 to measure tau (n = 23; entorhinal SUVR), and performed an object mnemonic discrimination task (n = 74). Structural MRI was processed with ASHS to obtain MTL ROIs (Fig1). Diffusion MRI was processed with FSL DTIfit to calculate mean diffusivity (MD), an index of cellular integrity. Tractography was performed with DSI Studio. ROI‐to‐ROI streamlines were calculated (Fig3A) and used for structural connectivity graph analyses of the MTL network. Analyses controlled for age and sex, with scan interval and education in relevant models.ResultEntorhinal MK6240 SUVR was associated with increased MD in transentorhinal cortex (BA35; r = 0.72, p&lt;0.001), entorhinal cortex (EC; r = 0.61, p = 0.004), and dentate gyrus (DG; r = 0.46, p = 0.04; Fig2A), while global FBP SUVR was associated with higher MD in EC (r = 0.25, p = 0.03). Worse mnemonic discrimination performance was associated with increased MD in DG (r = ‐0.29, p = 0.01), with trends in EC (r = ‐0.23, p = 0.06) and CA3 (r = ‐0.20, p = 0.09; Fig2B). The number of streamlines between EC and DG was not significantly associated with entorhinal MK6240 SUVR (r = ‐0.32, p = 0.17), FBP SUVR, or mnemonic discrimination (ps&gt;0.37). Lower global efficiency of the MTL network was associated with increased entorhinal MK6240 SUVR (r = ‐0.50, p = 0.025) yet better mnemonic discrimination performance (r = ‐0.28, p = 0.02; Fig3B).ConclusionEntorhinal tau pathology and memory performance are closely related to microstructural integrity and structural connectivity of the MTL, suggesting tau‐related MTL neurodegeneration may drive early memory decline.

Proteins involved in synapse organization are associated with longitudinal loss in gray matter network connectivity in prodromal AD

AbstractBackgroundLower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics.MethodData from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p&lt;0.05) were identified and examined for their biological relevance according to Gene Ontology.ResultConnectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif &lt;0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls.ConclusionProteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

Developmental plasticity of the structural network of the occipital cortex in congenital blindness.

The occipital cortex is the visual processing center in the mammalian brain. An unanswered scientific question pertains to the impact of congenital visual deprivation on the development of various profiles within the occipital network. To address this issue, we recruited 30 congenitally blind participants (8 children and 22 adults) as well as 31 sighted participants (10 children and 21 adults). Our investigation focused on identifying the gray matter regions and white matter connections within the occipital cortex, alongside behavioral measures, that demonstrated different developmental patterns between blind and sighted individuals. We discovered significant developmental changes in the gray matter regions and white matter connections of the occipital cortex among blind individuals from childhood to adulthood, in comparison with sighted individuals. Moreover, some of these structures exhibited cognitive functional reorganization. Specifically, in blind adults, the posterior occipital regions (left calcarine fissure and right middle occipital gyrus) showed reorganization of tactile perception, and the forceps major tracts were reorganized for braille reading. These plastic changes in blind individuals may be attributed to experience-dependent neuronal apoptosis, pruning, and myelination. These findings provide valuable insights into the longitudinal neuroanatomical and cognitive functional plasticity of the occipital network following long-term visual deprivation.

Differences in Intrinsic Gray Matter Connectivity and Their Genomic Underpinnings in Autism Spectrum Disorder

BackgroundAutism is a heterogeneous neurodevelopmental condition accompanied by differences in brain connectivity. Structural connectivity in autism has mainly been investigated within the white matter. However, many genetic variants associated with autism highlight genes related to synaptogenesis and axonal guidance, thus also implicating differences in intrinsic (i.e., gray matter) connections in autism. Intrinsic connections may be assessed in vivo via so-called intrinsic global and local wiring costs. MethodsHere, we examined intrinsic global and local wiring costs in the brain of 359 individuals with autism and 279 healthy control participants ages 6 to 30 years from the EU-AIMS LEAP (Longitudinal European Autism Project). FreeSurfer was used to derive surface mesh representations to compute the estimated length of connections required to wire the brain within the gray matter. Vertexwise between-group differences were assessed using a general linear model. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to link neuroanatomical differences to putative underpinnings. ResultsGroup differences in global and local wiring costs were predominantly observed in medial and lateral prefrontal brain regions, in inferior temporal regions, and at the left temporoparietal junction. The resulting neuroanatomical patterns were enriched for genes that had been previously implicated in the etiology of autism at genetic and transcriptomic levels. ConclusionsBased on intrinsic gray matter connectivity, the current study investigated the complex neuroanatomy of autism and linked between-group differences to putative genomic and/or molecular mechanisms to parse the heterogeneity of autism and provide targets for future subgrouping approaches.

Generative Sampling in Bundle Tractography using Autoencoders (GESTA).

Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true white matter pathways because some bundles are "harder-to-track" than others. This results in tractography reconstructions with poor white and gray matter spatial coverage. In this work, we propose a generative, autoencoder-based method, named GESTA (Generative Sampling in Bundle Tractography using Autoencoders), that produces streamlines achieving better spatial coverage. Compared to other deep learning methods, our autoencoder-based framework uses a single model to generate streamlines in a bundle-wise fashion, and does not require to propagate local orientations. GESTA produces new and complete streamlines for any given white matter bundle, including hard-to-track bundles. Applied on top of a given tractogram, GESTA is shown to be effective in improving the white matter volume coverage in poorly populated bundles, both on synthetic and human brain in vivo data. Our streamline evaluation framework ensures that the streamlines produced by GESTA are anatomically plausible and fit well to the local diffusion signal. The streamline evaluation criteria assess anatomy (white matter coverage), local orientation alignment (direction), and geometry features of streamlines, and optionally, gray matter connectivity. The GESTA framework offers considerable gains in bundle overlap using a reduced set of seeding streamlines with a 1.5x improvement for the "Fiber Cup", and 6x for the ISMRM 2015 Tractography Challenge datasets. Similarly, it provides a 4x white matter volume increase on the BIL&GIN callosal homotopic dataset, and successfully populates bundles on the multi-subject, multi-site, whole-brain in vivo TractoInferno dataset. GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.

CSF proteins involved in neuroplasticity are associated with altered whole brain connectivity in prodromal AD

AbstractBackgroundIndividuals with prodromal Alzheimer’s Disease (AD) and higher gray matter connectivity measures show slower decline, suggesting resilience to AD. However, the precise biological processes underlying preserved brain connections remains unclear. Here, we studied this question using cerebrospinal fluid (CSF) proteomics.MethodFrom the ADNI we selected 78 individuals with MCI and abnormal CSF amyloid (i.e., prodromal AD) and 34 with normal cognition and normal CSF amyloid levels for whom MRI and CSF proteomic data was available. For each T1 MRI scan we constructed gray matter networks using an automated pipeline (Tijms, Series et al. 2012) and calculated degree, connectivity density, clustering, path length and small‐world measures (i.e. gamma, lambda and small world coefficient). Linear models were used to test associations between each connectivity measure and CSF protein levels while correcting for age and gender. Proteins showing associations of p &lt; 0.1 were selected to find groups of proteins that are associated with biological processes according to Gene Ontology (GO). Proteomic profiles for each connectivity measure were compared to investigate overlap. Finally, we tested for enrichment of potential upstream drivers with the Enrichr tool.ResultIn total, 115 proteins were associated with altered connectivity in prodromal AD subjects (figure 1). The majority of proteins (n = 93, 80%) showed higher levels with stronger (i.e., resilient) brain networks characterized by increased degree, connectivity density, clustering, path length, gamma, lambda or small world coefficient values. In controls, 80 of these 93 proteins showed an opposite relationship, with lower levels relating to higher values for these connectivity measures, which suggests that increased levels in prodromal AD may indicate a compensatory mechanism to maintain strong brain connectivity. According to a pathway analysis in GO, these proteins associated with stronger brain networks were mainly involved in neuroplasticity pathways. A subsequent Enrichr analysis suggested involvement of upstream divers SUZ12 and REST, which are known to regulate neuroplasticity processes.ConclusionWe observed that higher CSF levels of neuroplasticity associated proteins were associated with higher brain connectivity values in prodromal AD, suggesting that plasticity mechanisms may play a role in resilience to AD pathology.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

Functional Covariance Connectivity of Gray and White Matter in Olfactory-Related Brain Regions in Parkinson's Disease.

Before the onset of motor symptoms, Parkinson’s disease (PD) involves dysfunction of the anterior olfactory nucleus and olfactory bulb, causing olfactory disturbance, commonly resulting in hyposmia in the early stages of PD. Accumulating evidence has shown that blood oxygen level dependent (BOLD) signals in white matter are altered by olfactory disorders and related stimuli, and the signal changes in brain white matter pathways show a certain degree of specificity, which can reflect changes of early olfactory dysfunction in Parkinson’s disease. In this study, we apply the functional covariance connectivity (FCC) method to decode FCC of gray and white matter in olfactory-related brain regions in Parkinson’s disease. Our results show that the dorsolateral prefrontal, anterior entorhinal cortex and fronto-orbital cortices in the gray matter have abnormal connectivity with the posterior corona radiata and superior corona radiata in white matter in patients with Parkinson’s hyposmia. The functional covariance connection strength (FCS) of the right dorsolateral prefrontal cortex and white matter, and the covariance connection strength of the left superior corona radiata and gray matter function have potential diagnostic value. These results demonstrate that alterations in FCC of gray and white matter in olfactory-related brain regions can reflect the change of olfactory function in the early stages of Parkinson’s disease, indicating that it could be a potential neuroimaging marker for early diagnosis.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline.

Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Altered cerebellar gray matter and cerebellar-cortex resting-state functional connectivity in patients with bipolar disorder Ⅰ

BackgroundBipolar disorder (BP) is a common psychiatric disorder characterized by extreme fluctuations in mood. Recent studies have indicated the involvement of cerebellum in the pathogenesis of BP. However, no study has focused on the precise role of cerebellum exclusively in patients with bipolar I disorder (BP-I). MethodsForty-five patients with BP-I and 40 healthy controls were recruited. All subjects underwent clinical evaluation and Magnetic Resonance diffusion Tension Imaging scans. For structural images, we used a spatially unbiased infratentorial template toolbox to isolate the cerebellum and then preformed voxel-based morphometry (VBM) analyses to assess the difference in cerebellar gray matter volume (GMV) between the two groups. For the functional images, we chose the clusters that survived from VBM analysis as seeds and performed functional connectivity (FC) analysis. Between-group differences were assessed using the independent Students t test or the nonparametric Mann‐Whitney U Test. For multiple comparisons, the results were further corrected with Gaussian random field (GRF) approach (voxel-level P < 0.001, cluster-level P < 0.05). ResultsCompared with healthy controls, BP-I patients showed significantly decreased GMV in left lobule V and left lobule VI (P < 0.05, GRF corrected). The FC of cerebellum with bilateral superior temporal gyrus, bilateral insula, bilateral rolandic operculum, right putamen, and left precentral gyrus was disrupted in BP-I patients (P < 0.05, GRF corrected). ConclusionsBP-I patients showed decreased cerebellar GMV and disrupted cerebellar-cortex resting-state FC. This suggests that cerebellar abnormalities may play an important role in the pathogenesis of BP-I.

Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment.

Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.

Differential gray matter connectivity correlates of CSF biomarkers: Results from the EPAD Cohort

AbstractBackgroundGray matter network (GMN) disruptions correlate with amyloid burden in the earliest stages of Alzheimer’s Disease (AD) pathology in cognitively unimpaired individuals. However, it remains unknown if and how hyperphosphorylated tau (p‐tau) influences GMNs. Here, we investigated the relation between GMNs and cerebrospinal fluid (CSF) p‐tau181 levels in non‐demented individuals from the European Prevention of Alzheimer Dementia (EPAD) cohort and evaluated whether this relationship is mediated by amyloid and Clinical Dementia Rating (CDR) status.MethodWe included 1198 participants from the EPAD v1500.0 release, who had both CSF biomarkers and structural MRI available. The cut‐off for amyloid‐β positivity (A+) was defined at &lt;1000 pg/mL. Single‐subject network properties were extracted from 3D‐T1w MRI (Figure 1). Regional values were computed for 117 AAL atlas regions. Linear models were used to investigate main effects and interactions between amyloid status and p‐tau181 levels on global GMN measures. A three‐way interaction with CDR was modeled. Age, sex, site, total intracranial volume, and network size were included as covariates. Analyses were repeated for local network properties across AAL regions and FDR‐adjusted.ResultSample characteristics are provided in Table 1. A+ individuals had lower degree, clustering, gamma, and small world coefficients, and higher betweenness centrality (Figure 2). Higher p‐tau181 levels were associated with lower values of degree, betweenness centrality, lambda, and path length (Figure 3). Lambda, path length, and clustering showed an interaction effect of amyloid status and CDR on their relationship with p‐tau181 levels: A‐ individuals with CDR 0 had lower path length and lambda in association with tau, while A+ CDR 0.5 individuals with lower clustering coefficient values showed higher p‐tau181 (Figure 4). No associations were found for other GMN measures. Regional analysis showed a relationship of p‐tau181 with local path length in the precuneus (Figure 5).ConclusionGMN alterations show distinct associations with p‐tau181 levels, partially affected by amyloid and CDR status. Non‐amyloid related alterations of path length might be linked to axonal tau‐related changes. Clustering alterations seem instead to be linked to amyloid deposition and resulting local synaptic disconnectivity.