Towards Refining Alzheimer\u27s Disease into Overlapping Subgroups

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder characterized by progressive anterograde amnesia, cerebral atrophy, and eventual death. Current treatment has limited efficacy and cannot decelerate the disease progression. Clinical trials targeting the removal of the neuropathological hallmarks of AD, including accu- mulation of amyloid plaques or neurofibrillary tangles, have failed to modify disease progression. Without new or innovative hypotheses, AD is poised to become a public health crisis within this decade. We present an alternative hypothesis—that AD is the result of multiple interrelated causalities. The intention of this manuscript is to initiate a discussion regarding these multiple causalities and their overlapping similarities. The idea of creating subgroups allows for better identification of biomarkers across a narrower patient population for improved pharmacotherapeutic opportunities. The interrelatedness of many of these proposed subgroups indicates the complexity of this disorder. However, it also supports that no one single factor may initiate the cascade of events

Resting-state brain information flow predicts cognitive flexibility in humans

The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain’s directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and topdown exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans

Temporal Changes in Local Functional Connectivity Density Reflect the Temporal Variability of the Amplitude of Low Frequency Fluctuations in Gray Matter

<div><p>Data-driven functional connectivity density (FCD) mapping is being increasingly utilized to assess brain connectomics at rest in the healthy brain and its disruption in neuropsychiatric diseases with the underlying assumption that the spatiotemporal hub distribution is stationary. However, recent studies show that functional connectivity is highly dynamic. Here we study the temporal variability of the local FCD (<i>l</i>FCD) at high spatiotemporal resolution (2-mm isotropic; 0.72s) using a sliding-window approach and ‘resting-state’ datasets from 40 healthy subjects collected under the Human Connectome Project. Prominent functional connectivity hubs in visual and posterior parietal cortices had pronounced temporal changes in local FCD. These dynamic patterns in the strength of the <i>l</i>FCD hubs occurred in cortical gray matter with high sensitivity (up to 85%) and specificity (> 85%) and showed high reproducibility (up to 72%) across sessions and high test-retest reliability (ICC(3,1) > 0.5). The temporal changes in <i>l</i>FCD predominantly occurred in medial occipitoparietal regions and were proportional to the strength of the connectivity hubs. The temporal variability of the <i>l</i>FCD was associated with the amplitude of the low frequency fluctuations (ALFF). Pure randomness did not account for the probability distribution of <i>l</i>FCD. Shannon entropy increased in proportion to the strength of the <i>l</i>FCD hubs suggesting high average flow of information per unit of time in the <i>l</i>FCD hubs, particularly in medial occipitoparietal regions. Thus, the higher dynamic range of the <i>l</i>FCD hubs is consistent with their role in the complex orchestration of interacting brain networks.</p></div

Effect of GSN.

<p>Average <i>l</i>FCD (A) and SD (C) maps across subjects with (G) and without (NG) GSN, with 0.08Hz or 0.15Hz low-pass filtering, and their statistical differences (two-sided t-score; B and D) superimposed on axial (right), sagittal (middle) and coronal (left) views of the cortical and subcortical gray matter template developed using the HCP structural scans.</p

Effects of head motion on FC dynamics.

<p>(A) Mean Fisher’s z-score values (top row) and their statistical significance across subjects superimposed on three orthogonal views of a gray matter template (t-test; bottom row), demonstrating the linear correlation between framewise displacements (FD) and the FC metrics (<i>l</i>FCD and ALFF) in the brain as a function of time. (B) Three orthogonal views showing the distribution in the brain of the group mean Fisher’s z-scores from partial correlation analyses (“Motion removed”; top row) and from standard Pearson correlation analyses (“With motion”; bottom row).</p

Temporal variability at the individual level.

<p>(A) Exemplary series of dynamic <i>l</i>FCD maps (in voxels; i.e. without grand mean global scaling) from a typical resting state HCP dataset superimposed on an axial view of the T1 weighted brain structure (top) and <i>l</i>FCD time courses (colored lines) corresponding to four different voxels from gray matter regions (colored arrows). The standard deviation (SD; in voxels) maps in B and C quantify the temporal dynamics of the <i>l</i>FCD metric in the brain for a single individual. Pipeline 4.</p

<i>l</i>FCD dynamics: Gray matter specificity and test-retest reliability.

<p>(A) Average sensitivity index for SD in subcortical and cortical gray matter and white matter, and average reproducibility and specificity indices across subjects for each of the processing pipelines in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154407#pone.0154407.g001" target="_blank">Fig 1</a>. (B) Two-way mixed single measures intraclass correlation ICC(3,1) maps at 2-mm isotropic resolution depicting regional variability in test-retest reliability for SD (pipeline 4). (C) Scatter plot showing the linear association of the mean between-subject SD-differences (MBSD) across voxels in overlapping (No GM) and non-overlapping (GM) gray matter for all potential pairs of subjects (pipeline 4). Error bars are standard deviations.</p

Image processing pipelines.

<p>Ten dynamic <i>l</i>FCD maps and 4 dynamic ALFF maps were computed for each subject, session, and phase encoding direction using 5 different pipelines (see text). A total of 1600 <i>l</i>FCD and 640 ALFF maps covering the whole brain (white matter and cerebrospinal fluid regions were not masked out to assess the strength of the <i>l</i>FCD in these regions) with 2-mm isotropic resolution and 91×109×91 voxels were computed using 160 HCP datasets with “minimal preprocessing” [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154407#pone.0154407.ref036" target="_blank">36</a>] from the Q1 release. Smoothing was not used to preserve the high spatial resolution of the resting-state functional datasets.</p