T. Yokoi

An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan

ABSTRACT This study aims to elucidate age‐related intrinsic brain volume changes over the adult lifespan using an unbiased data‐driven structural brain parcellation. Anatomical brain images from a cohort of 293 healthy volunteers ranging in age from 21 to 86 years were analyzed using independent component analysis (ICA). ICA‐based parcellation identified 192 component images, of which 174 (90.6%) showed a significant negative correlation with age and with some components being more vulnerable to aging effects than others. Seven components demonstrated a convex slope with aging; 3 components had an inverted U‐shaped trajectory, and 4 had a U‐shaped trajectory. Linear combination of 86 components provided reliable prediction of chronological age with a mean absolute prediction error of approximately 7.2 years. Structural co‐variation analysis showed strong interhemispheric, short‐distance positive correlations and long‐distance, inter‐lobar negative correlations. Estimated network measures either exhibited a U‐ or an inverted U‐shaped relationship with age, with the vertex occurring at approximately 45–50 years. Overall, these findings could contribute to our knowledge about healthy brain aging and could help provide a framework to distinguish the normal aging processes from that associated with age‐related neurodegenerative diseases. HighlightsAn unbiased, data‐driven brain parcellation was generated using anatomical images and ICA.Most parcels showed strong negative correlation with age with significant regional variations.Linear combination of parcels reliably predicted chronological age with mean error of 7.2 years.Structural co‐variation analysis showed positive and negative inter‐regional GM correlations.Structural network measures exhibited U‐ or inverted U‐shaped relation with age.

Corpus callosal involvement is correlated with cognitive impairment in multiple system atrophy

ObjectiveWe examined the anatomical involvement related to cognitive impairment in patients with multiple system atrophy (MSA).MethodsWe examined 30 patients with probable MSA and 15 healthy controls. All MSA patients were assessed by the Unified MSA-Rating scale and Addenbrooke’s Cognitive Examination-Revised (ACE-R). We classified 15 MSA patients with ACE-R scoresu2009>u200988 as having normal cognition (MSA–NC) and 15 with scoresu2009≤u200988 as having cognitive impairment (MSA–CI). All subjects underwent 3xa0T MRI scanning and were investigated using voxel-based morphometry and diffusion tensor imaging.ResultsBoth the MSA–NC and MSA–CI patients exhibited cerebellar but not cerebral atrophy in voxel-based morphometry compared to controls. In contrast, tract-based spatial statistics revealed widespread and significantly decreased fractional anisotropy (FA) values, as well as increased mean diffusivity, radial diffusivity, and axial diffusivity in both the cerebrum and cerebellum in MSA–CI patients compared to controls. MSA–NC patients also exhibited similar involvement of the cerebellum but less extensive involvement of the cerebrum compared with the MSA–CI patients. In particular, FA values in MSA–CI patients were significantly decreased in the anterior part of the left corpus callosum compared with those in MSA–NC patients. The mean FA values in the left anterior part of the corpus callosum were significantly correlated with total ACE-R scores and subscores (memory, fluency, and language) in MSA patients.ConclusionsDecreased FA values in the anterior corpus callosum showed a significant correlation with cognitive impairment in MSA.

Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson’s disease

Cognitive deficits in Parkinson’s disease (PD) are heterogeneous entities, but a relationship between the heterogeneity of cognitive deficits and resting-state network (RSN) changes remains elusive. In this study, we examined five sub-domain scores according to Addenbrooke’s Cognitive Examination-Revised (ACE-R) for the cognitive evaluation and classification of 72 non-demented patients with PD. Twenty-eight patients were classified as PD with normal cognition (PD-NC). The remaining 44 were subdivided into the following 2 groups using a hierarchical cluster analysis: 20 with a predominant decrease in memory (PD with amnestic cognitive deficits: PD-A) and 24 with good memory who exhibited a decrease in other sub-domains (PD with non-amnestic cognitive deficits: PD-NA). We used an independent component analysis of RS-fMRI data to investigate the inter-group differences of RSN. Compared to the controls, the PD-A showed lower FC within the ventral default mode network (vDMN) and the visuospatial network. On the other hand, the PD-NA showed lower FC within the visual networks and the cerebellum–brainstem network. Significant differences in the FC within the vDMN and cerebellum–brainstem network were observed between the PD-A and PD-NA, which provided a good discrimination between PD-A and PD-NA using a support vector machine. Distinct patterns of cognitive deficits correspond to different RSN changes.

Involvement of the Precuneus/Posterior Cingulate Cortex Is Significant for the Development of Alzheimer’s Disease: A PET (THK5351, PiB) and Resting fMRI Study

Background: Imaging studies in Alzheimer’s disease (AD) have yet to answer the underlying questions concerning the relationship among tau retention, neuroinflammation, network disruption and cognitive decline. We compared the spatial retention patterns of 18F-THK5351 and resting state network (RSN) disruption in patients with early AD and healthy controls. Methods: We enrolled 23 11C-Pittsburgh compound B (PiB)-positive patients with early AD and 24 11C-PiB-negative participants as healthy controls. All participants underwent resting state functional MRI and 18F-THK5351 PET scans. We used scaled subprofile modeling/principal component analysis (SSM/PCA) to reduce the complexity of multivariate data and to identify patterns that exhibited the largest statistical effects (variances) in THK5351 concentration in AD and healthy controls. Findings: SSM/PCA identified a significant spatial THK5351 pattern composed by mainly three clusters including precuneus/posterior cingulate cortex (PCC), right and left dorsolateral prefrontal cortex (DLPFC) which accounted for 23.6% of the total subject voxel variance of the data and had 82.6% sensitivity and 79.1% specificity in discriminating AD from healthy controls. There was a significant relationship between the intensity of the 18F-THK5351 covariation pattern and cognitive scores in AD. The spatial patterns of 18F-THK5351 uptake showed significant similarity with intrinsic functional connectivity, especially in the PCC network. Seed-based connectivity analysis from the PCC showed significant decrease in connectivity over widespread brain regions in AD patients. An evaluation of an autopsied AD patient with Braak V showed that 18F-THK5351 retention corresponded to tau deposition, monoamine oxidase-B (MAO-B) and astrogliosis in the precuneus/PCC. Interpretation: We identified an AD-specific spatial pattern of 18F-THK5351 retention in the precuneus/PCC, an important connectivity hub region in the brain. Disruption of the functional connections of this important network hub may play an important role in developing dementia in AD.