Structural brain network topology underpinning ADHD and response to methylphenidate treatment

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

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Structural brain network fingerprints of focal dystonia

Therapeutic Advances in Neurological Disorders ◽

10.1177/1756286419880664 ◽

2019 ◽

Vol 12 ◽

pp. 175628641988066 ◽

Cited By ~ 1

Author(s):

Venkata C. Chirumamilla ◽

Christian Dresel ◽

Nabin Koirala ◽

Gabriel Gonzalez-Escamilla ◽

Günther Deuschl ◽

...

Keyword(s):

Frontal Cortex ◽

Sensorimotor Cortex ◽

Temporal Cortex ◽

Brain Regions ◽

Brain Network ◽

Focal Dystonia ◽

Global Network ◽

Grey Matter Volume ◽

Structural Brain Network ◽

Structural Brain

Background: Focal dystonias are severe and disabling movement disorders of a still unclear origin. The structural brain networks associated with focal dystonia have not been well characterized. Here, we investigated structural brain network fingerprints in patients with blepharospasm (BSP) compared with those with hemifacial spasm (HFS), and healthy controls (HC). The patients were also examined following treatment with botulinum neurotoxin (BoNT). Methods: This study included matched groups of 13 BSP patients, 13 HFS patients, and 13 HC. We measured patients using structural-magnetic resonance imaging (MRI) at baseline and after one month BoNT treatment, at time points of maximal and minimal clinical symptom representation, and HC at baseline. Group regional cross-correlation matrices calculated based on grey matter volume were included in graph-based network analysis. We used these to quantify global network measures of segregation and integration, and also looked at local connectivity properties of different brain regions. Results: The networks in patients with BSP were more segregated than in patients with HFS and HC ( p < 0.001). BSP patients had increased connectivity in frontal and temporal cortices, including sensorimotor cortex, and reduced connectivity in the cerebellum, relative to both HFS patients and HC ( p < 0.05). Compared with HC, HFS patients showed increased connectivity in temporal and parietal cortices and a decreased connectivity in the frontal cortex ( p < 0.05). In BSP patients, the connectivity of the frontal cortex diminished after BoNT treatment ( p < 0.05). In contrast, HFS patients showed increased connectivity in the temporal cortex and reduced connectivity in cerebellum after BoNT treatment ( p < 0.05). Conclusions: Our results show that BSP patients display alterations in both segregation and integration in the brain at the network level. The regional differences identified in the sensorimotor cortex and cerebellum of these patients may play a role in the pathophysiology of focal dystonia. Moreover, symptomatic reduction of hyperkinesia by BoNT treatment was associated with different brain network fingerprints in both BSP and HFS patients.

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Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.715517 ◽

2021 ◽

Vol 13 ◽

Author(s):

Zhaoshun Jiang ◽

Yuxi Cai ◽

Xixue Zhang ◽

Yating Lv ◽

Mengting Zhang ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trial ◽

Cardiac Surgery ◽

Prefrontal Cortex ◽

Random Forest ◽

Resting State ◽

Brain Network ◽

Support Vector ◽

Whole Brain ◽

The Right

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

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No support for white matter microstructural properties and connectivity differences in the combined and inattentive adhd presentations

10.1101/2020.12.22.423936 ◽

2020 ◽

Author(s):

Jacqueline F. Saad ◽

Kristi R. Griffiths ◽

Michael R. Kohn ◽

Taylor A Braund ◽

Simon Clarke ◽

...

Keyword(s):

White Matter ◽

Functional Neuroimaging ◽

Brain Network ◽

Structural Level ◽

Significant Finding ◽

Brain Organization ◽

Brain White Matter ◽

Graph Properties ◽

Structural Architecture ◽

The Right

AbstractEvidence from functional neuroimaging studies support neural differences between the Attention Deficit Hyperactivity Disorder (ADHD) presentation types. It remains unclear if these neural deficits also manifest at the structural level. We have previously shown that the ADHD combined, and ADHD inattentive types demonstrate differences in graph properties of structural covariance suggesting an underlying difference in neuroanatomical organization. The goal of this study was to examine and validate white matter brain organization between the two subtypes using both scalar and connectivity measures of brain white matter. We used both tract-based spatial statistical (TBSS) and tractography analyses with network-based Statistics (NBS) and graph-theoretical analyses in a cohort of 35 ADHD participants (aged 8-17 years) defined using DSM-IV criteria as combined (ADHD-C) type (n=19) or as predominantly inattentive (ADHD-I) type (n=16), and 28 matched neurotypical controls. We performed TBSS analyses on scalar measures of fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity to assess differences in WM between ADHD types and controls. NBS and graph theoretical analysis of whole brain inter-regional tractography examined connectomic differences and brain network organization, respectively. None of the scalar measures significantly differed between ADHD types or relative to controls. Similarly, there were no tractography connectivity differences between the two subtypes and relative to controls using NBS. Global and regional graph measures were also similar between the groups. A single significant finding was observed for nodal degree between the ADHD-C and controls, in the right insula (corrected p=.029). Our result of no white matter differences between the subtypes is consistent with most previous findings. These findings together might suggest that the white matter structural architecture is largely similar between the DSM-based ADHD presentations.

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Changes of Structural Brain Network Following Repetitive Transcranial Magnetic Stimulation in Children With Bilateral Spastic Cerebral Palsy: A Diffusion Tensor Imaging Study

Frontiers in Pediatrics ◽

10.3389/fped.2020.617548 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wenxin Zhang ◽

Shang Zhang ◽

Min Zhu ◽

Jian Tang ◽

Xiaoke Zhao ◽

...

Keyword(s):

Cerebral Palsy ◽

Diffusion Tensor ◽

Repetitive Transcranial Magnetic Stimulation ◽

Inferior Frontal Gyrus ◽

Brain Network ◽

Spastic Cerebral Palsy ◽

Cingulate Gyrus ◽

Structural Brain Network ◽

The Right ◽

Structural Brain

Introduction: Bilateral spastic cerebral palsy (BSCP) is the most common subtype of cerebral palsy (CP), which is characterized by various motor and cognitive impairments, as well as emotional instability. However, the neural basis of these problems and how repetitive transcranial magnetic stimulation (rTMS) can make potential impacts on the disrupted structural brain network in BSCP remain unclear. This study was aimed to explore the topological characteristics of the structural brain network in BSCP following the treatment of rTMS.Methods: Fourteen children with BSCP underwent 4 weeks of TMS and 15 matched healthy children (HC) were enrolled. Diffusion tensor imaging (DTI) data were acquired from children with bilateral spastic cerebral palsy before treatment (CP1), children with bilateral spastic cerebral palsy following treatment (CP2) and HC. The graph theory analysis was applied to construct the structural brain network. Then nodal clustering coefficient (Ci) and shortest path length (Li) were measured and compared among groups.Results: Brain regions with significant group differences in Ci were located in the left precental gyrus, middle frontal gyrus, calcarine fissure, cuneus, lingual gyrus, postcentral gyrus, inferior parietal gyri, angular gyrus, precuneus, paracentral lobule and the right inferior frontal gyrus (triangular part), insula, posterior cingulate gyrus, precuneus, paracentral lobule, pallidum. In addition, significant differences were detected in the Li of the left precental gyrus, lingual gyrus, superior occipital gyrus, middle occipital gyrus, superior parietal gyrus, precuneus and the right median cingulate gyrus, posterior cingulate gyrus, hippocampus, putamen, thalamus. Post hoc t-test revealed that the CP2 group exhibited increased Ci in the right inferior frontal gyrus, pallidum and decreased Li in the right putamen, thalamus when compared with the CP1 group.Conclusion: Significant differences of node-level metrics were found in various brain regions of BSCP, which indicated a disruption in structural brain connectivity in BSCP. The alterations of the structural brain network provided a basis for understanding of the pathophysiological mechanisms of motor and cognitive impairments in BSCP. Moreover, the right inferior frontal gyrus, putamen, thalamus could potentially be biomarkers for predicting the efficacy of TMS.

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Structural brain network characteristics in patients with episodic and chronic migraine

10.21203/rs.3.rs-100065/v2 ◽

2021 ◽

Author(s):

Lars Michels ◽

Nabin Koirala ◽

Sergiu Groppa ◽

Roger Luechinger ◽

Andreas R Gantenbein ◽

...

Keyword(s):

Large Scale ◽

Structural Integrity ◽

Primary Headache ◽

Headache Disorder ◽

Brain Network ◽

Clustering Coefficient ◽

Global Network ◽

Support Vector ◽

Clinical Parameters ◽

Network Measures

Abstract Background: Migraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network. Methods: 19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters. Results: Network based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients’ group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients’ group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs. Conclusion: We found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain.

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Topological Aberrance of Structural Brain Network Provides Quantitative Markers of post-TBI Attention Deficits in Children

10.1101/2020.06.12.20129890 ◽

2020 ◽

Author(s):

Meng Cao ◽

Yuyang Luo ◽

Ziyan Wu ◽

Catherine A. Mazzola ◽

Arlene Goodman ◽

...

Keyword(s):

Temporal Lobe ◽

Critical Role ◽

Brain Network ◽

Attention Problems ◽

Clustering Coefficient ◽

Attention Deficits ◽

Topological Properties ◽

Local Efficiency ◽

Structural Brain Network ◽

Structural Brain

ABSTRACTTraumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attenpting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their associations with TBI-induced attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A).A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral associations were all corrected for multiple comparisons using Bonferroni method.Compare to controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, while significantly lower nodal clustering coefficient in left supramarginal gyrus as well as lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group.The results suggest that TBI-related structural re-modularity in the WM subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of TBI-A, and have the potential to serve as a biomarker guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals.

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Altered global brain network topology as a trait marker in patients with anorexia nervosa

Psychological Medicine ◽

10.1017/s0033291718004002 ◽

2019 ◽

Vol 50 (1) ◽

pp. 107-115 ◽

Cited By ~ 2

Author(s):

Daniel Geisler ◽

Viola Borchardt ◽

Ilka Boehm ◽

Joseph A. King ◽

Friederike I. Tam ◽

...

Keyword(s):

Anorexia Nervosa ◽

Resting State ◽

Network Architecture ◽

Brain Network ◽

Global Network ◽

Local Network ◽

Support Vector ◽

Graph Theoretic ◽

Trait Marker ◽

Global Brain

AbstractBackgroundResting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph–theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients.MethodsGraph–theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55).ResultsIndicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%.ConclusionsThis pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.

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Structural brain network characteristics in patients with episodic and chronic migraine

The Journal of Headache and Pain ◽

10.1186/s10194-021-01216-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lars Michels ◽

Nabin Koirala ◽

Sergiu Groppa ◽

Roger Luechinger ◽

Andreas R. Gantenbein ◽

...

Keyword(s):

Large Scale ◽

Structural Integrity ◽

Primary Headache ◽

Headache Disorder ◽

Brain Network ◽

Clustering Coefficient ◽

Global Network ◽

Support Vector ◽

Clinical Parameters ◽

Network Measures

Abstract Background Migraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network. Methods 19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters. Results Network based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients’ group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients’ group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs. Conclusion We found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain.

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