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Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.

Objective: Given the importance of advance care planning (ACP) in the context of a pandemic, we aimed to assess current adherence to local policy recommending ACP in all hospitalised adult patients with suspected or proven COVID-19 at Liverpool Hospital, Sydney, Australia., Design: A retrospective cohort study., Setting: A tertiary referral and teaching hospital., Participants: A select sample of adult patients admitted to Liverpool Hospital in 2019-2021 with suspected or proven COVID-19., Main Outcome Measures: Proportion of patients with documented ACP and format of ACP., Results: Amongst 209 patients with proven or suspected COVID-19 hospitalised between March 2019 through to September 2021, median frailty score was 3, the median Charlson Comorbidity Score was 4, median age of the patients was 71 years, and median length of hospital stay was 5 days (range 0-98 days). Almost all patients were tested for COVID-19 (n = 207, 99%) of which 15% (31) were positive. Fewer than a quarter of the patients had documented ACPs (50, 24%) and 17 patients had existing formal advance care directives. Patients who had ACP were older, more likely to be frail and more likely to have higher rates of comorbidity compared to those without ACP. ACP was more commonly discussed with family members (41/50) than patients (25/50) and others (5/50)., Conclusion: Adherence to the local ACP policy mandating such discussions was low. This reinforces the need for prioritising ACP discussions, especially for unwell patients such as those with COVID, likely involving further input to improve awareness and rates of formal documentation., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background/Objectives : Posterior circulation stroke (PCS) poses a diagnostic challenge due to the diverse and subtle clinical manifestations. While the FAST (Face, Arms, Speech, Time) mnemonic has proven effective in identifying anterior circulation stroke, its sensitivity to posterior events is less clear. Recently, the addition of Balance and Eyes to the mnemonic has been proposed as a more comprehensive tool for stroke recognition. Despite this, evidence directly comparing the effectiveness of BE-FAST and FAST in identifying PCS remains limited. Methods : A retrospective analysis was performed on stroke calls at a comprehensive stroke centre, Sydney, Australia. BE-FAST symptoms first assessed at an emergency department triage were recorded, along with automated acute computerised tomography perfusion (CTP) imaging findings. Haemorrhagic strokes were excluded from analysis. An ischaemic stroke diagnosis was confirmed 48-72 h later with magnetic resonance imaging (MRI) brain. The performance of 1. BE-FAST and FAST and 2. BE-FAST and CTP in the hyperacute detection of posterior circulation ischaemic stroke was compared. Results : Out of 164 identified ischaemic infarcts confirmed on MRIs, 46 were PCS. Of these, 27 were FAST-positive, while 45 were BE-FAST-positive. Overall, BE-FAST demonstrated a higher sensitivity compared to FAST in identifying PCS (97.8 vs. 58.7) but suffered from a lower specificity (10.0 vs. 39.8). Notably, 39.1% ( n = 18) of patients with PCS would have been missed if only FAST were used. Furthermore, of the 26 PCS negative on CTP, 25 were BE-FAST-positive, and 14 were FAST-positive. Conclusions : The incorporation of Balance and Eye assessments into the FAST protocol improves PCS detection, although may yield more false positives.

Parkinson's disease (PD) has two main pathological hallmarks, the loss of nigral dopamine neurons and the proteinaceous aggregations of ⍺-synuclein (⍺Syn) in neuronal Lewy pathology. These two co-existing features suggest a causative association between ⍺Syn aggregation and the underpinning mechanism of neuronal degeneration in PD. Both increased levels and post-translational modifications of ⍺Syn can contribute to the formation of pathological aggregations of ⍺Syn in neurons. Recent studies have shown that the protein is also expressed by multiple types of non-neuronal cells in the brain and peripheral tissues, suggesting additional roles of the protein and potential diversity in non-neuronal pathogenic triggers. It is important to determine (1) the threshold levels triggering ⍺Syn to convert from a biological to a pathologic form in different brain cells in PD; (2) the dominant form of pathologic ⍺Syn and the associated post-translational modification of the protein in each cell type involved in PD; and (3) the cell type associated biological processes impacted by pathologic ⍺Syn in PD. This review integrates these aspects and speculates on potential pathological mechanisms and their impact on neuronal and non-neuronal ⍺Syn in the brains of patients with PD., Competing Interests: Declaration of competing interest The authors claim that there are no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition., (© 2024. The Author(s).)

Brodmann area 8 (BA8) is traditionally defined as the prefrontal region of the human cerebrum just anterior to the premotor cortices and enveloping most of the superior frontal gyrus. Early studies have suggested the frontal eye fields are situated at its most caudal aspect, causing many to consider BA8 as primarily an ocular center which controls contralateral gaze and attention. However, years of refinement in cytoarchitectural studies have challenged this traditional anatomical definition, providing a refined definition of its boundaries with neighboring cortical areas and the presence of meaningful subdivisions. Furthermore, functional imaging studies have suggested its involvement in a diverse number of higher-order functions, such as motor, cognition, and language. Thus, our traditional working definition of BA8 has likely been insufficient to truly understand the complex structural and functional significance of this area. Recently, large-scale multi-modal neuroimaging approaches have allowed for improved mapping of the neural connectivity of the human brain. Insight into the structural and functional connectivity of the brain connectome, comprised of large-scale brain networks, has allowed for greater understanding of complex neurological functioning and pathophysiological diseases states. Simultaneously, the structural and functional connectivity of BA8 has recently been highlighted in various neuroimaging studies and detailed anatomic dissections. However, while Brodmann's nomenclature is still widely used today, such as for clinical discussions and the communication of research findings, the importance of the underlying connectivity of BA8 requires further review., Competing Interests: ND has no disclosures. OT was an employee of Omniscient Neurotechnology. MS was a co-founder of Omniscient Neurotechnology. No aspects related to these products were discussed in the current work. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dadario, Tanglay and Sughrue.)

Background: Despite efforts to improve targeting accuracy of the dorsolateral prefrontal cortex (DLPFC) as a repetitive transcranial magnetic stimulation (rTMS) target for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained., Objective: We sought to compare the patterns of functional connectivity from the DLPFC treatment site in patients with MDD who were TMS responders to those who were TMS non-responders., Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 37 participants before they underwent a course of rTMS to left Brodmann area 46. A novel machine learning method was utilized to identify brain regions associated with each item of the Beck's Depression Inventory II (BDI-II), and for 26 participants who underwent rTMS treatment over the left Brodmann area 46, identify regions differentiating rTMS responders and non-responders., Results: Nine parcels of the Human Connectome Project Multimodal Parcellation Atlas matched to at least three items of the Beck's Depression Inventory II (BDI-II) as predictors of response to rTMS, with many in the temporal, parietal and cingulate cortices. Additionally, pre-treatment mapping for 17 items of the BDI-II demonstrated significant variability in symptom to parcel mapping. When parcels associated with symptom presence and symptom resolution were compared, 15 parcels were uniquely associated with resolution (potential targets), and 12 parcels were associated with both symptom presence and resolution (blockers or biomarkers)., Conclusions: Machine learning approaches show promise for the development of pathoanatomical diagnosis and treatment algorithms for MDD. Prospective studies are required to facilitate clinical translation., Competing Interests: Conflict of interest Seven authors (HT, PN, OT, IY, LD, SD and MS) are employees of Omniscient Neurotechnology. SD and MS are also stakeholders of Omniscient Neurotechnology., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Locating the hand-motor-cortex (HMC) is an essential component within many neurosurgeries. Despite advancements in these localization methods there are still downfalls for each. Additionally, the importance of presurgical planning calls for increasingly accurate and efficient methods of locating specific cortical regions., Objective: In this study we aimed to test the ability of the Structural Connectivity Atlas (SCA), a machine-learning based method to parcellate the human cortex, to locate the HMC in a small cohort study., Methods: Using MRI and DTI images obtained from adult subjects (n = 11), personalized brain maps were created for each individual based on a SCA paired with the Brainnetome region for the HMC. Subjects received single pulse TMS, over the HMC region through the use of a neuronavigation system. If they responded with motor movement, this was recorded. The SCA identified HMC region was compared to the visual-determined HMC through identifying the Omega fold on the Precentral Gyrus, which was completed by a trained neuroanatomist. A Kendall's Tau B correlation was conducted between anatomical match and visual movement., Results: This study concluded that the SCA was capable of locating the HMC in healthy and distorted brains. Overall, the SCA defined the anatomical area of the HMC in 90 % of subjects and triggered a motor response in 61 %., Conclusion: The SCA could be suitable for incorporation into presurgical planning practices due to its ability to map anatomically abnormal brains. Further studies on larger cohorts and targeting different areas of cortex could be beneficial., Competing Interests: Conflict of interest KO, AM, PN, PR, HT, IT, AJ, LD, OT, MS & SD are employees of Omniscient Neurotechnology., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective: This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology., Methods: T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia., Results: The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology., Conclusion: We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome., Competing Interests: Declaration of Competing Interest Six authors (HT, PN, OT, IY, SD and MS) are employees of Omniscient Neurotechnology. SD and MS are co-founders and directors of Omniscient Neurotechnology., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient-specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study., Methods: We used the resting-state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN). The most abnormal areas of connectivity within these regions were selected for subsequent targeted TMS treatment by a machine learning based on an anomalous functional connectivity detection matrix. Areas with mostly hyperconnectivity were stimulated with continuous theta burst stimulation and the converse with intermittent theta burst stimulation. An image-guided accelerated theta burst stimulation paradigm was used for treatment., Results: Areas 8Av and PGs demonstrated consistent abnormalities, particularly in the left hemisphere. Significant improvements were demonstrated in anxiety symptoms, and few, minor complications were reported (fatigue (n = 2) and headache (n = 1))., Conclusions: Our study suggests that a left-lateralized DMN is likely the primary functional network disturbed in anxiety-related disorders, which can be improved by identifying and targeting abnormal regions with a rapid, data-driven, agile aTBS treatment on an individualized basis., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Background: Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment., Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness., Results: Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model's classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation., Conclusions: Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine., (© 2023. The Author(s).)

Background and Purpose: Mapping the topology of the visual system is critical for understanding how complex cognitive processes like reading can occur. We aim to describe the connectivity of the visual system to understand how the cerebrum accesses visual information in the lateral occipital lobe., Methods: Using meta-analytic software focused on task-based functional MRI studies, an activation likelihood estimation (ALE) of the visual network was created. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE to identify the hub-like regions of the visual network. Diffusion Spectrum Imaging-based fiber tractography was performed to determine the structural connectivity of these regions with extraoccipital cortices., Results: The fundus of the superior temporal sulcus (FST) and parietal area H (PH) were identified as hub-like regions for the visual network. FST and PH demonstrated several areas of coactivation beyond the occipital lobe and visual network. Furthermore, these parcellations were highly interconnected with other cortical regions throughout extraoccipital cortices related to their nonvisual functional roles. A cortical model demonstrating connections to these hub-like areas was created., Conclusions: FST and PH are two hub-like areas that demonstrate extensive functional coactivation and structural connections to nonvisual cerebrum. Their structural interconnectedness with language cortices along with the abnormal activation of areas commonly located in the temporo-occipital region in dyslexic individuals suggests possible important roles of FST and PH in the integration of information related to language and reading. Future studies should refine our model by examining the functional roles of these hub areas and their clinical significance., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Multiple system atrophy (MSA) is a debilitating movement disorder with unknown etiology. Patients present characteristic parkinsonism and/or cerebellar dysfunction in the clinical phase, resulting from progressive deterioration in the nigrostriatal and olivopontocerebellar regions. MSA patients have a prodromal phase subsequent to the insidious onset of neuropathology. Therefore, understanding the early pathological events is important in determining the pathogenesis, which will assist with developing disease-modifying therapy. Although the definite diagnosis of MSA relies on the positive post-mortem finding of oligodendroglial inclusions composed of α-synuclein, only recently has MSA been verified as an oligodendrogliopathy with secondary neuronal degeneration. We review up-to-date knowledge of human oligodendrocyte lineage cells and their association with α-synuclein, and discuss the postulated mechanisms of how oligodendrogliopathy develops, oligodendrocyte progenitor cells as the potential origins of the toxic seeds of α-synuclein, and the possible networks through which oligodendrogliopathy induces neuronal loss. Our insights will shed new light on the research directions for future MSA studies.

Objective: Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes., Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test., Results: The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models., Conclusions: Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary., Competing Interests: NM, KO, OT, IY, SD, and MS are employees of Omniscient Neurotechnology. IY, SD, and MS are also stakeholders of Omniscient Neurotechnology. XH and XZ are the employees of Xijia Medical Technology Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bian, Huo, Liu, Mansouri, Tanglay, Young, Osipowicz, Hu, Zhang, Doyen, Sughrue and Liu.)

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery., Competing Interests: Michael E. Sughrue is the Chief Medical Officer, co-founder, and shareholder for Omniscient Neurotechnology. Onur Tanglay and Isabella M. Young are employees of Omniscient Neurotechnology. Isabella M. Young is also a shareholder of Omniscient Neurotechnology. Other authors do not report any conflicts of interest.

Intervertebral disc degeneration (IVDD), a widely recognized cause of lower back pain, is the leading cause of disability worldwide. A myriad of preclinical in vivo animal models of IVDD have been described in the literature. There is a need for critical evaluation of these models to better inform researchers and clinicians to optimize study design and ultimately, enhance experimental outcomes. The purpose of this study was to conduct an extensive systematic literature review to report the variability of animal species, IVDD induction method, and experimental timepoints and endpoints used in in vivo IVDD preclinical research. A systematic literature review of peer-reviewed manuscripts featured on PubMed and EMBASE databases was conducted in accordance with PRISMA guidelines. Studies were included if they reported an in vivo animal model of IVDD and included details of the species used, how disc degeneration was induced, and the experimental endpoints used for analysis. Two-hundred and fifty-nine (259) studies were reviewed. The most common species, IVDD induction method and experimental endpoint used was rodents(140/259, 54.05%), surgery (168/259, 64.86%) and histology (217/259, 83.78%), respectively. Experimental timepoint varied greatly between studies, ranging from 1 week (dog and rodent models), to >104 weeks in dog, horse, monkey, rabbit, and sheep models. The two most common timepoints used across all species were 4 weeks (49 manuscripts) and 12 weeks (44 manuscripts). A comprehensive discussion of the species, methods of IVDD induction and experimental endpoints is presented. There was great variability across all categories: animal species, method of IVDD induction, timepoints and experimental endpoints. While no animal model can replicate the human scenario, the most appropriate model should be selected in line with the study objectives to optimize experimental design, outcomes and improve comparisons between studies., Competing Interests: None to report., (© 2022 The Authors. JOR Spine published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Parkinson's disease (PD) is a movement disorder characterized by the early loss of nigrostriatal dopaminergic pathways producing significant network changes impacting motor coordination. Recently three motor stages of PD have been proposed (a silent period when nigrostriatal loss begins, a prodromal motor period with subtle focal manifestations, and clinical PD) with evidence that motor cortex abnormalities occur to produce clinical PD[8]. We directly assess structural changes in the primary motor cortex and corticospinal tract using parallel analyses of longitudinal clinical and cross-sectional pathological cohorts thought to represent different stages of PD. 18F-FP-CIT positron emission tomography and subtle motor features identified patients with idiopathic rapid-eye-movement sleep behaviour disorder (n = 8) that developed prodromal motor signs of PD. Longitudinal diffusion tensor imaging before and after the development of prodromal motor PD showed higher fractional anisotropy in motor cortex and corticospinal tract compared to controls, indicating adaptive structural changes in motor networks in concert with nigrostriatal dopamine loss. Histological analyses of the white matter underlying the motor cortex showed progressive disorientation of axons with segmental replacement of neurofilaments with α-synuclein, enlargement of myelinating oligodendrocytes and increased density of their precursors. There was no loss of neurons in the motor cortex in early or late pathologically confirmed motor PD compared to controls, although there were early cortical increases in neuronal neurofilament light chain and myelin proteins in association with α-synuclein accumulation. Our results collectively provide evidence of a direct impact of PD on primary motor cortex and its output pathways that begins in the prodromal motor stage of PD with structural changes confirmed in early PD. These adaptive structural changes become considerable as the disease advances potentially contributing to motor PD., (© 2022. The Author(s).)

Objective: Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities., Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups., Results: Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls., Conclusion: Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia., Competing Interests: NM, KO, OT, IY, SD, and MS were employed by Omniscient Neurotechnology. IY, SD, and MS were also stakeholders of Omniscient Neurotechnology. XZ was employed by the Shenzhen Xijia Medical Technology Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Shen, Lu, Zhang, Yan, Mansouri, Osipowicz, Tanglay, Young, Doyen, Lu, Zhang, Sughrue and Wang.)

Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy., Materials and Methods: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up., Results: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change., Conclusion: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets., Competing Interests: NM, KO, OT, IY, SD, and MS were employed by Omniscient Neurotechnology. IY, SD, and MS were also stakeholders of Omniscient Neurotechnology. XZ was employed by Shenzhen Xijia Medical Technology Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lu, Zhang, Yan, Mansouri, Tanglay, Osipowicz, Joyce, Young, Zhang, Doyen, Sughrue and He.)

Background: The salience network (SN) is a transitory mediator between active and passive states of mind. Multiple cortical areas, including the opercular, insular, and cingulate cortices have been linked in this processing, though knowledge of network connectivity has been devoid of structural specificity., Objective: The current study sought to create an anatomically specific connectivity model of the neural substrates involved in the salience network., Methods: A literature search of PubMed and BrainMap Sleuth was conducted for resting-state and task-based fMRI studies relevant to the salience network according to PRISMA guidelines. Publicly available meta-analytic software was utilized to extract relevant fMRI data for the creation of an activation likelihood estimation (ALE) map and relevant parcellations from the human connectome project overlapping with the ALE data were identified for inclusion in our SN model. DSI-based fiber tractography was then performed on publicaly available data from healthy subjects to determine the structural connections between cortical parcellations comprising the network., Results: Nine cortical regions were found to comprise the salience network: areas AVI (anterior ventral insula), MI (middle insula), FOP4 (frontal operculum 4), FOP5 (frontal operculum 5), a24pr (anterior 24 prime), a32pr (anterior 32 prime), p32pr (posterior 32 prime), and SCEF (supplementary and cingulate eye field), and 46. The frontal aslant tract was found to connect the opercular-insular cluster to the middle cingulate clusters of the network, while mostly short U-fibers connected adjacent nodes of the network., Conclusion: Here we provide an anatomically specific connectivity model of the neural substrates involved in the salience network. These results may serve as an empiric basis for clinical translation in this region and for future study which seeks to expand our understanding of how specific neural substrates are involved in salience processing and guide subsequent human behavior., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

The Gerstmann syndrome is a constellation of neurological deficits that include agraphia, acalculia, left-right discrimination and finger agnosia. Despite a growing interest in this clinical phenomenon, there remains controversy regarding the specific neuroanatomic substrates involved. Advancements in data-driven, computational modelling provides an opportunity to create a unified cortical model with greater anatomic precision based on underlying structural and functional connectivity across complex cognitive domains. A literature search was conducted for healthy task-based functional MRI and PET studies for the four cognitive domains underlying Gerstmann's tetrad using the electronic databases PubMed, Medline, and BrainMap Sleuth (2.4). Coordinate-based, meta-analytic software was utilized to gather relevant regions of interest from included studies to create an activation likelihood estimation (ALE) map for each cognitive domain. Machine-learning was used to match activated regions of the ALE to the corresponding parcel from the cortical parcellation scheme previously published under the Human Connectome Project (HCP). Diffusion spectrum imaging-based tractography was performed to determine the structural connectivity between relevant parcels in each domain on 51 healthy subjects from the HCP database. Ultimately 102 functional MRI studies met our inclusion criteria. A frontoparietal network was found to be involved in the four cognitive domains: calculation, writing, finger gnosis, and left-right orientation. There were three parcels in the left hemisphere, where the ALE of at least three cognitive domains were found to be overlapping, specifically the anterior intraparietal area, area 7 postcentral (7PC) and the medial intraparietal sulcus. These parcels surround the anteromedial portion of the intraparietal sulcus. Area 7PC was found to be involved in all four domains. These regions were extensively connected in the intraparietal sulcus, as well as with a number of surrounding large-scale brain networks involved in higher-order functions. We present a tractographic model of the four neural networks involved in the functions which are impaired in Gerstmann syndrome. We identified a 'Gerstmann Core' of extensively connected functional regions where at least three of the four networks overlap. These results provide clinically actionable and precise anatomic information which may help guide clinical translation in this region, such as during resective brain surgery in or near the intraparietal sulcus, and provides an empiric basis for future study., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD., Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests., Results: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment., Conclusion: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD., Competing Interests: HT, PN, OT, IY, KO, SD, and MS are employees of Omniscient Neurotechnology, and they provided technical assistance with the machine learning aspects of the project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dong, Fu, Li, Zhang, Liu, Xiao, Taylor, Nicholas, Tanglay, Young, Osipowicz, Sughrue, Doyen and Li.)

There is considerable interest in developing effective tools to detect Alzheimer's Disease (AD) early in its course, prior to clinical progression [...].

Purpose Advances in neuroimaging have provided an understanding of the precuneus'(PCu) involvement in functions such as visuospatial processing and cognition. While the PCu has been previously determined to be apart of a higher-order default mode network (DMN), recent studies suggest the presence of possible dissociations from this model in order to explain the diverse functions the PCu facilitates, such as in episodic memory. An improved structural model of the white-matter anatomy of the PCu can demonstrate its unique cerebral connections with adjacent regions which can provide additional clarity on its role in integrating information across higher-order cerebral networks like the DMN. Furthermore, this information can provide clinically actionable anatomic information that can support clinical decision making to improve neurologic outcomes such as during cerebral surgery. Here, we sought to derive the relationship between the precuneus and underlying major white-mater bundles by characterizing its macroscopic connectivity. Methods Structural tractography was performed on twenty healthy adult controls from the Human Connectome Project (HCP) utilizing previously demonstrated methodology. All precuneus connections were mapped in both cerebral hemispheres and inter-hemispheric differences in resultant tract volumes were compared with an unpaired, corrected Mann-Whitney U test and a laterality index (LI) was completed. Ten postmortem dissections were then performed to serve as ground truth by using a modified Klingler technique with careful preservation of relevant white matter bundles. Results The precuneus is a heterogenous cortical region with five major types of connections that were present bilaterally. (1) Short association fibers connect the gyri of the precuneus and connect the precuneus to the superior parietal lobule and the occipital cortex. (2) Four distinct parts of the cingulum bundle connect the precuneus to the frontal lobe and the temporal lobe. (3) The middle longitudinal fasciculus from the precuneus connects to the superior temporal gyrus and the dorsolateral temporal pole. (4) Parietopontine fibers travel as part of the corticopontine fibers to connect the precuneus to pontine regions. (5) An extensive commissural bundle connects the precuneus bilaterally. Conclusion We present a summary of the anatomic connections of the precuneus as part of an effort to understand the function of the precuneus and highlight key white-matter pathways to inform surgical decision-making. Our findings support recent models suggesting unique fiber connections integrating at the precuneus which may suggest finer subsystems of the DMN or unique networks, but further study is necessary to refine our model in greater quantitative detail., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: A systematic review was conducted on published data of subacute combined degeneration (SCD) from B12 deficiency to investigate potential prognostic indicators of final ambulatory function in affected patients. TYPE: Systematic review., Literature Survey: Records published from 1999 through 2018 were searched on the electronic databases MEDLINE, PUBMED, and SCOPUS. The publication language was restricted to English and French., Methodology: Records that described cases of SCD from B12 deficiency in patients ≥16 years of age at onset were included. From a final total of 66 cases of SCD identified, ambulation scores were assigned based on the clinical descriptions reported. Patient characteristics, clinical manifestations, and ambulatory function were reported descriptively. Subanalyses on potential prognostic indicators were performed., Synthesis: Greater ambulatory function at diagnosis was associated with recovery of normal ambulatory function at follow-up (P < .001). Nearly 90% of patients walking unsupported at diagnosis made a complete recovery regardless of etiology. For patients initially walking with support, 22% of cases from impaired B12 digestion/absorption made a complete recovery compared with ≥50% of cases from other etiologies. For patients initially requiring a wheelchair, 33% of cases from impaired digestion/absorption were able to walk with support compared with ≥50% of cases from other etiologies. The total B12 administered over the course of treatment was correlated with improved ambulation (P = .024) for all patients, with a higher B12 replacement regimen associated with better outcomes in patients who were unable to walk unsupported at diagnosis (number needed to treat = 4)., Conclusions: Initial ambulatory function may be a useful clinical marker of the severity of spinal cord dysfunction and final functional outcome. Etiological risk factors influenced both clinical manifestations and final walking ability in SCD, suggesting a distinct pathophysiological mechanism among the causes. In addition, a more intensive B12 replacement regimen may improve ultimate ambulatory function in advanced cases of SCD., (© 2021 American Academy of Physical Medicine and Rehabilitation.)

Purpose: Applying graph theory to the human brain has the potential to help prognosticate the impacts of intracerebral surgery. Eigenvector (EC) and PageRank (PR) centrality are two related, but uniquely different measures of nodal centrality which may be utilized together to reveal varying neuroanatomical characteristics of the brain connectome., Methods: We obtained diffusion neuroimaging data from a healthy cohort (UCLA consortium for neuropsychiatric phenomics) and applied a personalized parcellation scheme to them. We ranked parcels based on weighted EC and PR, and then calculated the difference (EP difference) and correlation between the two metrics. We also compared the difference between the two metrics to the clustering coefficient., Results: While EC and PR were consistent for top and bottom ranking parcels, they differed for mid-ranking parcels. Parcels with a high EC centrality but low PR tended to be in the medial temporal and temporooccipital regions, whereas PR conferred greater importance to multi-modal association areas in the frontal, parietal and insular cortices. The EP difference showed a weak correlation with clustering coefficient, though there was significant individual variation., Conclusions: The relationship between PageRank and eigenvector centrality can identify distinct topological characteristics of the brain connectome such as the presence of unimodal or multimodal association cortices. These results highlight how different graph theory metrics can be used alone or in combination to reveal unique neuroanatomical features for further clinical study., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The superior parietal lobule (SPL) is involved in somatosensory and visuospatial integration with additional roles in attention, written language, and working memory. A detailed understanding of the exact location and nature of associated white matter tracts could improve surgical decisions and subsequent postoperative morbidity related to surgery in and around this gyrus., Objective: To characterize the fiber tracts of the SPL based on relationships to other well-known neuroanatomic structures through diffusion spectrum imaging (DSI)-based fiber tracking validated by gross anatomical dissection as ground truth., Methods: Neuroimaging data of 10 healthy, adult control subjects was obtained from a publicly accessible database published in Human Connectome Project for subsequent tractographic analyses. White matter tracts were mapped between both cerebral hemispheres, and a lateralization index was calculated based on resultant tract volumes. Post-mortem dissections of 10 cadavers identified the location of major tracts and validated our tractography results based on qualitative visual agreement., Results: We identified 9 major connections of the SPL: U-fiber, superior longitudinal fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, middle longitudinal fasciculus, extreme capsule, vertical occipital fasciculus, cingulum, and corpus callosum. There was no significant fiber lateralization detected., Conclusion: The SPL is an important region implicated in a variety of tasks involving visuomotor and visuospatial integration. Improved understanding of the fiber bundle anatomy elucidated in this study can provide invaluable information for surgical treatment decisions related to this region., (© Congress of Neurological Surgeons 2021.)

Purpose: Venous thromboembolism (VTE) is a well-known problem in patients with intracranial tumors, especially high-grade gliomas. Optimal management of VTE complications is critical given that the development of deep vein thrombosis (DVT) and/or pulmonary embolism can exacerbate medical comorbidities and increase mortality. However, little is known about the optimum time to initiate post-operative anticoagulant prophylaxis. Therefore, there is a keen interest amongst neurosurgeons to develop evidence-based protocols to prevent VTE in post-operative brain tumor patients., Methods: We retrospectively identified adult patients who underwent elective craniotomy for intracranial tumor resection between 2012 and 2017. Patients were categorized according to the time at which they began receiving prophylactic enoxaparin in the immediate post-operative period, within one day (POD 1), two days (POD 2), three days (POD 3), five days (POD 5), or seven days (POD 7)., Results: A total of 1087 patients had a craniotomy for intracranial tumor resection between 2012 and 2017. Multivariate binomial logistic regression analysis demonstrated that initiation of prophylactic enoxaparin within 72 h of surgery was protective against the likelihood of developing a lower extremity DVT (OR: 0.32; CI: 0.10-0.95; p = 0.049) while controlling for possible risk factors for DVTs identified on univariate analysis. Furthermore, complication rates between the anticoagulation and non-anticoagulation groups were not statistically significant., Conclusion: Initiating anticoagulant prophylaxis with subcutaneous enoxaparin sodium 40 mg once per day within 72 h of surgery can be done safely while reducing the risk of developing lower extremity DVT., (Copyright © 2021. Published by Elsevier B.V.)

Background: The middle temporal gyrus (MTG) is understood to play a role in language-related tasks such as lexical comprehension and semantic cognition. However, a more specific understanding of its key white matter connections could promote the preservation of these functions during neurosurgery., Objective: To provide a detailed description of the underlying white matter tracts associated with the MTG to improve semantic preservation during neurosurgery., Methods: Tractography was performed using diffusion imaging obtained from 10 healthy adults from the Human Connectome Project. All tracts were mapped between cerebral hemispheres with a subsequent laterality index calculated based on resultant tract volumes. Ten postmortem dissections were performed for ex vivo validation of the tractography based on qualitative visual agreement., Results: We identified 2 major white matter bundles leaving the MTG: the inferior longitudinal fasciculus and superior longitudinal fasciculus. In addition to long association fibers, a unique linear sequence of U-shaped fibers was identified, possibly representing a form of visual semantic transfer down the temporal lobe., Conclusion: We elucidate the underlying fiber-bundle anatomy of the MTG, an area highly involved in the brain's language network. Improved understanding of the unique, underlying white matter connections in and around this area may augment our overall understanding of language processing as well as the involvement of higher order cerebral networks like the default mode network in these functions., (© Congress of Neurological Surgeons 2021.)

Background: The middle frontal gyrus (MFG) is involved in attention, working memory, and language-related processing. A detailed understanding of the subcortical white matter tracts connected within the MFG can facilitate improved navigation of white matter lesions in and around this gyrus and explain the postoperative morbidity after surgery. We aimed to characterize the fiber tracts within the MFG according to their connection to neuroanatomic structures through the use of diffusion spectrum imaging-based fiber tractography and validate the findings by gross anatomic dissection for qualitative visual agreement., Methods: Tractography analysis was completed using diffusion imaging data from 10 healthy, adult subjects enrolled in the Human Connectome Project. We assessed the MFG as a whole component according to its fiber connectivity with other neural regions. Mapping was completed on all tracts within both hemispheres, with the resultant tract volumes used to calculate a lateralization index. A modified Klingler technique was used on 10 postmortem dissections to demonstrate the location and orientation of the major tracts., Results: Two major connections of the MFG were identified: the superior longitudinal fasciculus, which connects the MFG to parts of the inferior parietal lobule, posterior temporal lobe, and lateral occipital cortex; and the inferior fronto-occipital fasciculus, which connected the MFG to the lingual gyrus and cuneus. Intra- and intergyral short association, U-shaped fibers were also identified., Conclusions: Subcortical white matter pathways integrated within the MFG include the superior longitudinal fasciculus and inferior fronto-occipital fasciculus. The MFG is implicated in a variety of tasks involving attention and memory, making it an important cortical region. The postoperative neurologic outcomes related to surgery in and around the MFG could be clarified in the context of the anatomy of the fiber bundles highlighted in the present study., (Copyright © 2021. Published by Elsevier Inc.)

Introduction: The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked anatomic specificity. Using attentional task-based fMRI studies, we built a neuroanatomical model of this network., Methods: One hundred and fifty-five task-based fMRI studies related to categorization of visual words and objects, and auditory words and stories were used to generate an activation likelihood estimation (ALE). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the semantic network based on the cortical parcellation scheme previously published under the Human Connectome Project. Deterministic fiber tractography was performed on 25 randomly chosen subjects from the Human Connectome Project, to determine the connectivity of the cortical parcellations comprising the network., Results: The ALE analysis demonstrated fourteen left hemisphere cortical regions to be a part of the semantic network: 44, 45, 55b, IFJa, 8C, p32pr, SFL, SCEF, 8BM, STSdp, STSvp, TE1p, PHT, and PBelt. These regions showed consistent interconnections between parcellations. Notably, the anterior temporal pole, a region often implicated in semantic function, was absent from our model., Conclusions: We describe a preliminary cortical model for the underlying structural connectivity of the semantic network. Future studies will further characterize the neurotractographic details of the semantic network in the context of medical application., (© 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Background: The parahippocampal gyrus is understood to have a role in high cognitive functions including memory encoding and retrieval and visuospatial processing. A detailed understanding of the exact location and nature of associated white tracts could significantly improve postoperative morbidity related to declining capacity. Through diffusion tensor imaging-based fiber tracking validated by gross anatomic dissection as ground truth, we have characterized these connections based on relationships to other well-known structures., Methods: Diffusion imaging from the Human Connectome Project for 10 healthy adult controls was used for tractography analysis. We evaluated the parahippocampal gyrus as a whole based on connectivity with other regions. All parahippocampal gyrus tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes., Results: We identified 2 connections of the parahippocampal gyrus: inferior longitudinal fasciculus and cingulum. Lateralization of the cingulum was detected (P < 0.05)., Conclusions: The parahippocampal gyrus is an important center for memory processing. Subtle differences in executive functioning following surgery for limbic tumors may be better understood in the context of the fiber-bundle anatomy highlighted by this study., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: The default mode network (DMN) is an important mediator of passive states of mind. Multiple cortical areas, such as the anterior cingulate cortex, posterior cingulate cortex, and lateral parietal lobe, have been linked in this processing, though knowledge of network connectivity had limited tractographic specificity., Methods: Using resting-state fMRI studies related to the DMN, we generated an activation likelihood estimation (ALE). We built a tractographical model of this network based on the cortical parcellation scheme previously published under the Human Connectome Project. DSI-based fiber tractography was performed to determine the structural connections between cortical parcellations comprising the network., Results: Seventeen cortical regions were found to be part of the DMN: 10r, 31a, 31pd, 31pv, a24, d23ab, IP1, p32, POS1, POS2, RSC, PFm, PGi, PGs, s32, TPOJ3, and v23ab. These regions showed consistent interconnections between adjacent parcellations, and the cingulum was found to connect the anterior and posterior cingulate clusters within the network., Conclusions: We present a preliminary anatomic model of the default mode network. Further studies may refine this model with the ultimate goal of clinical application., (© 2020 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Background: The inferior temporal gyrus (ITG) is known to be involved in high-cognitive functions, including visual and language comprehensions and emotion regulation. A detailed understanding of the nature of association fibers could significantly improve postoperative morbidity related to declining capacity. Through diffusion spectrum imaging-based fiber tracking, we have characterized these connections on the basis of their relationships to other cortical areas., Methods: Diffusion spectrum images from 10 healthy adults of the Human Connectome Project were randomly selected and used for tractography analysis. We evaluated the ITG as a whole based on connectivity with other regions. All ITG tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes., Results: We identified 5 major connections of the ITG: U-fiber, inferior longitudinal fasciculus, vertical occipital fasciculus, arcuate fasciculus, and uncinate fasciculus. There was no fiber lateralization detected., Conclusions: This study highlights the principal white-matter pathways of the ITG and demonstrates key underlying connections. We present a summary of the relevant clinical anatomy for this region of the cerebrum as part of a larger effort to understand it in its entirety., (Copyright © 2020. Published by Elsevier Inc.)

Introduction: The auditory network plays an important role in interaction with the environment. Multiple cortical areas, such as the inferior frontal gyrus, superior temporal gyrus and adjacent insula have been implicated in this processing. However, understanding of this network's connectivity has been devoid of tractography specificity., Methods: Using attention task-based functional magnetic resonance imaging (MRI) studies, an activation likelihood estimation (ALE) of the auditory network was generated. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE in the Montreal Neurological Institute coordinate space, and visually assessed for inclusion in the network. Diffusion spectrum MRI-based fiber tractography was performed to determine the structural connections between cortical parcellations comprising the network., Results: Fifteen cortical regions were found to be part of the auditory network: areas 44 and 8C, auditory area 1, 4, and 5, frontal operculum area 4, the lateral belt, medial belt and parabelt, parietal area F centromedian, perisylvian language area, retroinsular cortex, supplementary and cingulate eye field and the temporoparietal junction area 1. These regions showed consistent interconnections between adjacent parcellations. The frontal aslant tract was found to connect areas within the frontal lobe, while the arcuate fasciculus was found to connect the frontal and temporal lobe, and subcortical U-fibers were found to connect parcellations within the temporal area. Further studies may refine this model with the ultimate goal of clinical application., Competing Interests: Declaration of Competing Interest Dr Sughrue is the Chief Medical Officer of Omniscient Neurotechnology, however this does not pose a conflict of interest in this study. The other authors have no conflicts of interest., (Copyright © 2020. Published by Elsevier B.V.)