The neural network basis of altered decision‐making in patients with amyotrophic lateral sclerosis

Imai, Kazunori; Masuda, Mitsuharu; Watanabe, Haruo; Ogura, Aya; Ohdake, Reiko; Tanaka, Yasuhiro; Kato, Toshiyasu; Kawabata, Kazuya; Riku, Yuichi; Hara, Kazuhiro; Nakamura, Ryoichi; Atsuta, Naoki; Bagarinao, Epifanio; Katahira, Kentaro; Ohira, Hideki; Katsuno, Masahisa; Sobue, Gen

doi:10.1002/acn3.51185

Cited by 11 publications

(7 citation statements)

References 42 publications

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“…Similar findings were observed in patients with FTLD ( Ogura et al, 2019 ; Rittman et al, 2019 ; Chen et al, 2020 ; Imai et al, 2020 ). We also studied patients with ALS, which is thought to reflect the early stage of FTLD.…”

Section: Dementia and Functional Network: Positioning Of Hubssupporting

confidence: 86%

“…Tau and inflammation accumulated in the precuneus (Utevsky et al, 2014) and posterior cingulate gyrus (Leech and Sharp, 2014), which are the hubs of the default mode network; the associated network disruption correlated with disease onset. Similar findings were observed in patients with FTLD (Ogura et al, 2019;Rittman et al, 2019;Chen et al, 2020;Imai et al, 2020). We also studied patients with ALS, which is thought to reflect the early stage of FTLD.…”

Section: Dementia and Functional Network: Positioning Of Hubssupporting

confidence: 78%

“…In addition, we examined the decision-making disorder characteristic of FTLD, using probability inversion learning, and found that significantly more ALS patients selected a decision-making style that seemed to go their way. The degree of abnormality was associated with decreased functional connectivity of the anterior cingulate gyrus/frontal pole, a typical hub ( Imai et al, 2020 ).…”

Section: Dementia and Functional Network: Positioning Of Hubsmentioning

confidence: 99%

“…ACE-R evaluates five cognitive subdomains, including orientation/attention, memory, verbal fluency, language and visuospatial ability ( Mioshi et al, 2006 ), and has been reported to have excellent sensitivities and specificities (>0.8) for the diagnosis of MCI and dementia ( Livingston et al, 2017 ). In addition to aging studies, we have also employed similar brain network analysis to examine network alterations in patients with neurodegenerative disorders such as AD ( Yokoi et al, 2018 ), amyotrophic lateral sclerosis (ALS) FTLD ( Ogura et al, 2019 ; Imai et al, 2020 ), Parkinson’s disease with mild cognitive decline ( Kawabata et al, 2018 ) and others.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Neural Network Changes in Normal Aging and Early Dementia

Watanabe

Bagarinao

Maesawa

et al. 2021

Front. Aging Neurosci.

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To understand the mechanisms underlying preserved and impaired cognitive function in healthy aging and dementia, respectively, the spatial relationships of brain networks and mechanisms of their resilience should be understood. The hub regions of the brain, such as the multisensory integration and default mode networks, are critical for within- and between-network communication, remain well-preserved during aging, and play an essential role in compensatory processes. On the other hand, these brain hubs are the preferred sites for lesions in neurodegenerative dementias, such as Alzheimer’s disease. Disrupted primary information processing networks, such as the auditory, visual, and sensorimotor networks, may lead to overactivity of the multisensory integration networks and accumulation of pathological proteins that cause dementia. At the cellular level, the brain hub regions contain many synapses and require a large amount of energy. These regions are rich in ATP-related gene expression and had high glucose metabolism as demonstrated on positron emission tomography (PET). Importantly, the number and function of mitochondria, which are the center of ATP production, decline by about 8% every 10 years. Dementia patients often have dysfunction of the ubiquitin-proteasome and autophagy-lysosome systems, which require large amounts of ATP. If there is low energy supply but the demand is high, the risk of disease can be high. Imbalance between energy supply and demand may cause accumulation of pathological proteins and play an important role in the development of dementia. This energy imbalance may explain why brain hub regions are vulnerable to damage in different dementias. Here, we review (1) the characteristics of gray matter network, white matter network, and resting state functional network changes related to resilience in healthy aging, (2) the mode of resting state functional network disruption in neurodegenerative dementia, and (3) the cellular mechanisms associated with the disruption.

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Section: Dementia and Functional Network: Positioning Of Hubssupporting

confidence: 86%

Section: Dementia and Functional Network: Positioning Of Hubssupporting

confidence: 78%

Section: Dementia and Functional Network: Positioning Of Hubsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characteristics of Neural Network Changes in Normal Aging and Early Dementia

Watanabe

Bagarinao

Maesawa

et al. 2021

Front. Aging Neurosci.

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“…Thus, contrast images per dimension (contrast vector with 1 for the dimension modulator, 0 for all other regressors) were then entered into a group-level F-test to accommodate 2-tailed effects (i.e., positive or negative slopes that differed from zero). F-maps were FDR cluster-corrected (p<.05) with a heightthreshold of p<.001 (but due to limited coverage for the first function dimension and fourth vision dimension, a slightly more lenient height-threshold of p<.005 (FDR cluster-corrected p<.05) was used, as previously motivated (e.g., [138][139][140]. In short, the resulting F-maps show which brain areas demonstrate modulation sensitivity to each specific dimension.…”

Section: Similarity Ratings and Dimension Extractionmentioning

confidence: 99%

Neural and behavioral signatures of the multidimensionality of manipulable object processing

Almeida

Fracasso

Kristensen

et al. 2023

Preprint

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Understanding how we recognize everyday objects requires unravelling the variables that govern the way we think about objects and the way in which our representations are organized neurally. A major hypothesis is that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Here, we explored, behaviorally and neurally, the multidimensionality of object processing. We focused on within-domain object information as a proxy for the kinds of object decision tasks we typically engage in our daily lives — e.g., identifying a knife from other types of manipulable objects such as spoons, axes or screwdrivers. To do so, we extracted object-related dimensions from subjective human judgments on a set of objects from a particular object domain — i.e., manipulable objects. We demonstrated that the extracted dimensions are cognitively interpretable — i.e., participants are able to label them; are cognitively relevant for manipulable object processing — i.e., categorization decisions are guided by these dimensions; and are important for the neural organization of knowledge — i.e., they are good predictors of the neural signals elicited by manipulable objects. This shows that multidimensionality is a hallmark of the organization of object knowledge in the brain.

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