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2018 ; 8
(1
): 133
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Shared endo-phenotypes of default mode dsfunction in attention
deficit/hyperactivity disorder and autism spectrum disorder
#MMPMID30018328
Kernbach JM
; Satterthwaite TD
; Bassett DS
; Smallwood J
; Margulies D
; Krall S
; Shaw P
; Varoquaux G
; Thirion B
; Konrad K
; Bzdok D
Transl Psychiatry
2018[Jul]; 8
(1
): 133
PMID30018328
show ga
Categorical diagnoses from the Diagnostic and Statistical Manual of Mental
Disorders (DSM) or International Classification of Diseases (ICD) manuals are
increasingly found to be incongruent with emerging neuroscientific evidence that
points towards shared neurobiological dysfunction underlying attention
deficit/hyperactivity disorder and autism spectrum disorder. Using resting-state
functional magnetic resonance imaging data, functional connectivity of the
default mode network, the dorsal attention and salience network was studied in
1305 typically developing and diagnosed participants. A transdiagnostic
hierarchical Bayesian modeling framework combining Indian Buffet Processes and
Latent Dirichlet Allocation was proposed to address the urgent need for objective
brain-derived measures that can acknowledge shared brain network dysfunction in
both disorders. We identified three main variation factors characterized by
distinct coupling patterns of the temporoparietal cortices in the default mode
network with the dorsal attention and salience network. The brain-derived factors
were demonstrated to effectively capture the underlying neural dysfunction shared
in both disorders more accurately, and to enable more reliable diagnoses of
neurobiological dysfunction. The brain-derived phenotypes alone allowed for a
classification accuracy reflecting an underlying neuropathology of 67.33%
(+/-3.07) in new individuals, which significantly outperformed the 46.73%
(+/-3.97) accuracy of categorical diagnoses. Our results provide initial evidence
that shared neural dysfunction in ADHD and ASD can be derived from conventional
brain recordings in a data-led fashion. Our work is encouraging to pursue a
translational endeavor to find and further study brain-derived phenotypes, which
could potentially be used to improve clinical decision-making and optimize
treatment in the future.
|Adolescent
[MESH]
|Attention Deficit Disorder with Hyperactivity/*physiopathology
[MESH]