Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases.

[1]  Alexa B. Roggeveen,et al.  Large-scale gamma-band phase synchronization and selective attention. , 2008, Cerebral cortex.

[2]  Cornelis J. Stam,et al.  Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: The interplay of density, connectivity cost and life-time trajectory , 2015, NeuroImage.

[3]  A. Schulze-Bonhage,et al.  Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing , 2018, Journal of neural engineering.

[4]  P. Krukow,et al.  Quantitative and Qualitative Comparison of EEG-Based Neural Network Organization in Two Schizophrenia Groups Differing in the Duration of Illness and Disease Burden: Graph Analysis With Application of the Minimum Spanning Tree , 2018, Clinical EEG and neuroscience.

[5]  M. Portero-Otín,et al.  Early and gender-specific differences in spinal cord mitochondrial function and oxidative stress markers in a mouse model of ALS , 2016, Acta neuropathologica communications.

[6]  H. Berendse,et al.  Disrupted Brain Network Topology in Parkinson's Disease: a Longitudinal Meg Study , 2014 .

[7]  A. Storkey,et al.  Reduced structural connectivity within a prefrontal‐motor‐subcortical network in amyotrophic lateral sclerosis , 2015, Journal of magnetic resonance imaging : JMRI.

[8]  Jing Zhu,et al.  A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering , 2017, Complex..

[9]  T. Shallice,et al.  Right prefrontal cortex and episodic memory retrieval: a functional MRI test of the monitoring hypothesis. , 1999, Brain : a journal of neurology.

[10]  Arjan Hillebrand,et al.  Disrupted brain network topology in Parkinson's disease: a longitudinal magnetoencephalography study. , 2014, Brain : a journal of neurology.

[11]  Thomas F. Münte,et al.  A neurophysiological analysis of working memory in amyotrophic lateral sclerosis , 2011, Brain Research.

[12]  Edwin van Dellen,et al.  The minimum spanning tree: An unbiased method for brain network analysis , 2015, NeuroImage.

[13]  D. Glahn,et al.  Beyond hypofrontality: A quantitative meta‐analysis of functional neuroimaging studies of working memory in schizophrenia , 2005, Human brain mapping.

[14]  David E. J. Linden,et al.  Neuroimaging in Psychiatry: From Bench to Bedside , 2009, Front. Hum. Neurosci..

[15]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[16]  J. Cedarbaum,et al.  The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function , 1999, Journal of the Neurological Sciences.

[17]  Adriano Chiò,et al.  The heterogeneity of amyotrophic lateral sclerosis: a possible explanation of treatment failure. , 2007, Current medicinal chemistry.

[18]  Stavros Dimitriadis,et al.  Simple and difficult mathematics in children: A minimum spanning tree EEG network analysis , 2014, Neuroscience Letters.

[19]  O. Hardiman,et al.  Neurophysiological markers of network dysfunction in neurodegenerative diseases , 2018, NeuroImage: Clinical.

[20]  Haruka Dan,et al.  Individual classification of ADHD children by right prefrontal hemodynamic responses during a go/no-go task as assessed by fNIRS , 2015, NeuroImage: Clinical.

[21]  L. Goldstein,et al.  Verbal fluency and executive dysfunction in amyotrophic lateral sclerosis (ALS) , 2000, Neuropsychologia.

[22]  Dongchuan Yu,et al.  Weak network efficiency in young children with Autism Spectrum Disorder: Evidence from a functional near-infrared spectroscopy study , 2016, Brain and Cognition.

[23]  G. Tedeschi,et al.  Brain functional networks become more connected as amyotrophic lateral sclerosis progresses: a source level magnetoencephalographic study , 2018, NeuroImage: Clinical.

[24]  Martijn P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks , 2013, NeuroImage.

[25]  B. Miller,et al.  Are amyotrophic lateral sclerosis patients cognitively normal? , 2003, Neurology.

[26]  G. F. González,et al.  Graph analysis of EEG resting state functional networks in dyslexic readers , 2016, Clinical Neurophysiology.

[27]  C. Beaulieu,et al.  White matter structural network abnormalities underlie executive dysfunction in amyotrophic lateral sclerosis , 2017, Human brain mapping.

[28]  Nimish J. Thakore,et al.  Depression in ALS in a large self-reporting cohort , 2016, Neurology.

[29]  Dieter Vaitl,et al.  Relationship between regional hemodynamic activity and simultaneously recorded EEG‐theta associated with mental arithmetic‐induced workload , 2007, Human brain mapping.

[30]  T. S. Jackson,et al.  Theory of minimum spanning trees. I. Mean-field theory and strongly disordered spin-glass model. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  A. Hillebrand,et al.  EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis , 2016, Scientific Reports.

[32]  M. Bastin,et al.  Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis. , 2013, Brain : a journal of neurology.

[33]  R. J. Deligani,et al.  Multimodal exploration of non-motor neural functions in ALS patients using simultaneous EEG-fNIRS recording , 2019, Journal of neural engineering.

[34]  Jian Wang,et al.  Altered Brain Network in Amyotrophic Lateral Sclerosis: A Resting Graph Theory-Based Network Study at Voxel-Wise Level , 2016, Front. Neurosci..

[35]  Keum-Shik Hong,et al.  Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity , 2013, Journal of biomedical optics.

[36]  J. Veldink,et al.  Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis , 2014, Human brain mapping.

[37]  Cellular,et al.  Ganong's review of medical physiology , 2016 .

[38]  Shimon Even,et al.  Graph Algorithms: Depth-First Search , 2011 .

[39]  Massimo Filippi,et al.  Divergent brain network connectivity in amyotrophic lateral sclerosis , 2013, Neurobiology of Aging.

[40]  Mickaël Causse,et al.  Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS , 2017, Scientific Reports.

[41]  Christa Neuper,et al.  Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic , 2011, Medical & Biological Engineering & Computing.

[42]  R. D. de Haan,et al.  The cognitive profile of ALS: a systematic review and meta-analysis update , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[43]  P. Leigh,et al.  Prevalence of depression in a 12‐month consecutive sample of patients with ALS , 2007, European journal of neurology.

[44]  D. Krusienski,et al.  Impaired auditory evoked potentials and oscillations in frontal and auditory cortex of a schizophrenia mouse model , 2016, The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry.

[45]  Larissa C Schudlo,et al.  Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest , 2014, Journal of neural engineering.

[46]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[47]  R. J. Deligani,et al.  Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[48]  Joanne Wuu,et al.  Altered cortical beta‐band oscillations reflect motor system degeneration in amyotrophic lateral sclerosis , 2016, Human brain mapping.

[49]  Ke Chen,et al.  Patterns of Spontaneous Brain Activity in Amyotrophic Lateral Sclerosis: A Resting-State fMRI Study , 2012, PloS one.

[50]  M. Sawan,et al.  Denoising fNIRS Signals to Enhance Brain Imaging Diagnosis , 2013, 2013 29th Southern Biomedical Engineering Conference.

[51]  Steven Knight,et al.  Integration of structural and functional magnetic resonance imaging in amyotrophic lateral sclerosis. , 2011, Brain : a journal of neurology.

[52]  Bruce Tidor,et al.  MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets , 2009, Bioinform..

[53]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[54]  T. Münte,et al.  Relation of neuropsychological and magnetic resonance findings in amyotrophic lateral sclerosis: evidence for subgroups , 1997, Clinical Neurology and Neurosurgery.

[55]  Cedric E. Ginestet,et al.  Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.

[56]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[57]  Corinne A. Bareham,et al.  Bedside EEG predicts longitudinal behavioural changes in disorders of consciousness , 2020, NeuroImage: Clinical.

[58]  Junichi Ushiba,et al.  The correlation between motor impairments and event-related desynchronization during motor imagery in ALS patients , 2012, BMC Neuroscience.

[59]  N. Smyrnis,et al.  Frontal lobe dysfunction in amyotrophic lateral sclerosis , 2002, Journal of the Neurological Sciences.

[60]  K. Lyons,et al.  Brain activity during dual task gait and balance in aging and age-related neurodegenerative conditions: A systematic review , 2019, Experimental Gerontology.

[61]  Alan Bundy,et al.  Depth-First Search , 1984 .

[62]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[63]  W GEETS [Classification of epilepsy]. , 1950, Le Scalpel.

[64]  M. P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks. , 2013, NeuroImage.

[65]  M. Turner,et al.  Does interneuronal dysfunction contribute to neurodegeneration in amyotrophic lateral sclerosis? , 2012, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[66]  Fabrizio Esposito,et al.  Interaction between aging and neurodegeneration in amyotrophic lateral sclerosis , 2012, Neurobiology of Aging.

[67]  C. Stam Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[68]  Andrew Zalesky,et al.  Minimum spanning tree analysis of the human connectome , 2018, Human brain mapping.

[69]  J. Alvarez,et al.  Executive Function and the Frontal Lobes: A Meta-Analytic Review , 2006, Neuropsychology Review.

[70]  S. Rose Selective attention , 1992, Nature.

[71]  Stanislas Dehaene,et al.  Arithmetic and the Brain This Review Comes from a Themed Issue on Cognitive Neuroscience Edited the Intraparietal Sulcus and Number Sense Number Sense in the Animal Brain , 2022 .

[72]  Ki-Young Jung,et al.  Classification of epilepsy types through global network analysis of scalp electroencephalograms. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[73]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[74]  C. Rorden,et al.  Preservation of structural brain network hubs is associated with less severe post-stroke aphasia. , 2015, Restorative neurology and neuroscience.

[75]  Ruben Schmidt,et al.  Correlation between structural and functional connectivity impairment in amyotrophic lateral sclerosis , 2014, Human brain mapping.

[76]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[77]  Volkmar Glauche,et al.  Processing Pathways in Mental Arithmetic—Evidence from Probabilistic Fiber Tracking , 2013, PloS one.

[78]  R. Goetz,et al.  Cognitive-behavioral screening reveals prevalent impairment in a large multicenter ALS cohort , 2016, Neurology.

[79]  Adriano Chiò,et al.  Cognitive correlates in amyotrophic lateral sclerosis: a population-based study in Italy , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[80]  N. Smyrnis,et al.  Selective Attention and the Three-Process Memory Model for the Interpretation of Verbal Free Recall in Amyotrophic Lateral Sclerosis , 2012, Journal of the International Neuropsychological Society.

[81]  Guy A. Dumont,et al.  Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy , 2014, Front. Hum. Neurosci..

[82]  F. Marrosu,et al.  Functional brain connectivity analysis in amyotrophic lateral sclerosis: an EEG source-space study , 2018 .

[83]  K. Wirdefeldt,et al.  Depression in amyotrophic lateral sclerosis , 2016, Neurology.

[84]  Angus W. MacDonald,et al.  Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia , 2015, Neuropsychologia.

[85]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[86]  Yu Sun,et al.  Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study , 2019, Neurophotonics.

[87]  Dongchuan Yu,et al.  Novel analysis of fNIRS acquired dynamic hemoglobin concentrations: application in young children with autism spectrum disorder. , 2018, Biomedical optics express.

[88]  A. C. Papanicolaou,et al.  Modular Patterns of Phase Desynchronization Networks During a Simple Visuomotor Task , 2015, Brain Topography.

[89]  P N Leigh,et al.  Frontal lobe dysfunction in amyotrophic lateral sclerosis. A PET study. , 1996, Brain : a journal of neurology.

[90]  A. Mirelman,et al.  The Role of the Frontal Lobe in Complex Walking Among Patients With Parkinson’s Disease and Healthy Older Adults , 2016, Neurorehabilitation and neural repair.