Brain activity is contingent on neuropsychological function in an fMRI study of Verbal Working Memory in Amyotrophic Lateral Sclerosis

AbstractAmyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that causes progressive degeneration of neurons in motor and non-motor regions, affecting multiple cognitive domains. In order to contribute to the growing research field that employs structural and functional neuroimaging to investigate the effect of ALS on different working memory components, we conducted a functional magnetic resonance imaging (fMRI) study exploring the localization and intensity of alterations in neural activity. Being the first study to specifically address verbal working memory via fMRI in the context of ALS, we employed the verbal n-back task with 0-back and 2-back conditions. Despite ALS patients showing unimpaired accuracies and reaction times, there was significantly increased brain activity of frontotemporal and parietal regions in the 2-back minus 0-back contrast in patients compared to controls. This increased brain activity was largely associated with a better neuropsychological performance within the ALS group, suggesting a compensatory effect. This study therefore adds to the current knowledge on neural correlates of working memory in ALS and contributes to a more nuanced understanding of hyperactivity during cognitive processes in fMRI studies of ALS.

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Brain activity is contingent on neuropsychological function in an fMRI study of Verbal Working Memory in Amyotrophic Lateral Sclerosis

European Journal of Neurology ◽

10.1111/ene.14957 ◽

2021 ◽

Author(s):

Xenia Kobeleva ◽

Judith Machts ◽

Maria Veit ◽

Stefan Vielhaber ◽

Susanne Petri ◽

...

Keyword(s):

Working Memory ◽

Amyotrophic Lateral Sclerosis ◽

Brain Activity ◽

Verbal Working Memory ◽

Neuropsychological Function ◽

Fmri Study ◽

Lateral Sclerosis

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Patterns of Spontaneous Brain Activity in Amyotrophic Lateral Sclerosis: A Resting-State fMRI Study

PLoS ONE ◽

10.1371/journal.pone.0045470 ◽

2012 ◽

Vol 7 (9) ◽

pp. e45470 ◽

Cited By ~ 44

Author(s):

ChunYan Luo ◽

Qin Chen ◽

Rui Huang ◽

XuePing Chen ◽

Ke Chen ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Spontaneous Brain Activity ◽

Fmri Study ◽

Lateral Sclerosis

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Skipping Breakfast Affects the Early Steps of Cognitive Processing

Journal of Psychophysiology ◽

10.1027/0269-8803/a000214 ◽

2019 ◽

Vol 33 (2) ◽

pp. 109-118

Author(s):

Andrés Antonio González-Garrido ◽

Jacobo José Brofman-Epelbaum ◽

Fabiola Reveca Gómez-Velázquez ◽

Sebastián Agustín Balart-Sánchez ◽

Julieta Ramos-Loyo

Keyword(s):

Working Memory ◽

Cognitive Processing ◽

Task Difficulty ◽

Brain Activity ◽

Reaction Times ◽

Brain Potentials ◽

Brain Functioning ◽

Attentional Processes ◽

Electrical Brain Activity ◽

Skipping Breakfast

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.

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Difference in cortical activity of lumbar- and bulbar-form of Amyotrophic lateral sclerosis: an fMRI study

Klinische Neurophysiologie ◽

10.1055/s-0029-1216212 ◽

2009 ◽

Vol 40 (01) ◽

Author(s):

K Kollewe ◽

K Krampfl ◽

A Samii ◽

R Dengler ◽

T Münte ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Cortical Activity ◽

Fmri Study ◽

Lateral Sclerosis

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Brain Activity Elicited by Emotional Stimuli During a Verbal Working Memory Task: A Comparison of Healthy Adults and Patients with Chronic Schizophrenia

NeuroImage ◽

10.1016/s1053-8119(09)71772-0 ◽

2009 ◽

Vol 47 ◽

pp. S166

Author(s):

MT Diaz ◽

G He ◽

S Gadde ◽

C Bellion ◽

A Belger ◽

...

Keyword(s):

Working Memory ◽

Brain Activity ◽

Memory Task ◽

Verbal Working Memory ◽

Chronic Schizophrenia ◽

Healthy Adults ◽

Emotional Stimuli

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Neuroinflammation in Amyotrophic Lateral Sclerosis and Frontotemporal Dementia and the Interest of Induced Pluripotent Stem Cells to Study Immune Cells Interactions With Neurons

Frontiers in Molecular Neuroscience ◽

10.3389/fnmol.2021.767041 ◽

2021 ◽

Vol 14 ◽

Author(s):

Elise Liu ◽

Léa Karpf ◽

Delphine Bohl

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Immune System ◽

Frontotemporal Dementia ◽

Immune Cells ◽

Immune Cell ◽

Current Knowledge ◽

Cell Types ◽

Induced Pluripotent ◽

Different Cell Types ◽

Lateral Sclerosis

Inflammation is a shared hallmark between amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). For long, studies were conducted on tissues of post-mortem patients and neuroinflammation was thought to be only bystander result of the disease with the immune system reacting to dying neurons. In the last two decades, thanks to improving technologies, the identification of causal genes and the development of new tools and models, the involvement of inflammation has emerged as a potential driver of the diseases and evolved as a new area of intense research. In this review, we present the current knowledge about neuroinflammation in ALS, ALS-FTD, and FTD patients and animal models and we discuss reasons of failures linked to therapeutic trials with immunomodulator drugs. Then we present the induced pluripotent stem cell (iPSC) technology and its interest as a new tool to have a better immunopathological comprehension of both diseases in a human context. The iPSC technology giving the unique opportunity to study cells across differentiation and maturation times, brings the hope to shed light on the different mechanisms linking neurodegeneration and activation of the immune system. Protocols available to differentiate iPSC into different immune cell types are presented. Finally, we discuss the interest in studying monocultures of iPS-derived immune cells, co-cultures with neurons and 3D cultures with different cell types, as more integrated cellular approaches. The hope is that the future work with human iPS-derived cells helps not only to identify disease-specific defects in the different cell types but also to decipher the synergistic effects between neurons and immune cells. These new cellular tools could help to find new therapeutic approaches for all patients with ALS, ALS-FTD, and FTD.

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Glial Cells—The Strategic Targets in Amyotrophic Lateral Sclerosis Treatment

Journal of Clinical Medicine ◽

10.3390/jcm9010261 ◽

2020 ◽

Vol 9 (1) ◽

pp. 261 ◽

Cited By ~ 2

Author(s):

Tereza Filipi ◽

Zuzana Hermanova ◽

Jana Tureckova ◽

Ondrej Vanatko ◽

Miroslava Anderova

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Motor Neurons ◽

Current Knowledge ◽

Gene Mutations ◽

Cell Types ◽

In Vivo Models ◽

Cell Therapies ◽

Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease, which is characterized by the degeneration of motor neurons in the motor cortex and the spinal cord and subsequently by muscle atrophy. To date, numerous gene mutations have been linked to both sporadic and familial ALS, but the effort of many experimental groups to develop a suitable therapy has not, as of yet, proven successful. The original focus was on the degenerating motor neurons, when researchers tried to understand the pathological mechanisms that cause their slow death. However, it was soon discovered that ALS is a complicated and diverse pathology, where not only neurons, but also other cell types, play a crucial role via the so-called non-cell autonomous effect, which strongly deteriorates neuronal conditions. Subsequently, variable glia-based in vitro and in vivo models of ALS were established and used for brand-new experimental and clinical approaches. Such a shift towards glia soon bore its fruit in the form of several clinical studies, which more or less successfully tried to ward the unfavourable prognosis of ALS progression off. In this review, we aimed to summarize current knowledge regarding the involvement of each glial cell type in the progression of ALS, currently available treatments, and to provide an overview of diverse clinical trials covering pharmacological approaches, gene, and cell therapies.

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What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?

Journal of Personalized Medicine ◽

10.3390/jpm10040247 ◽

2020 ◽

Vol 10 (4) ◽

pp. 247

Author(s):

Christina Vasilopoulou ◽

Andrew P. Morris ◽

George Giannakopoulos ◽

Stephanie Duguez ◽

William Duddy

Keyword(s):

Machine Learning ◽

Amyotrophic Lateral Sclerosis ◽

Genetic Architecture ◽

Molecular Mechanisms ◽

Current Knowledge ◽

Regulatory Elements ◽

Specific Information ◽

Learning Models ◽

Lateral Sclerosis ◽

Machine Learning Models

Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.

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