Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe.

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.

Symptom-circuit mappings of the schizophrenia connectome.

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.

Functional connectivity reveals different brain networks underlying the idiopathic foreign accent syndrome.

Foreign accent syndrome (FAS) is characterized by new onset speech that is perceived as foreign. Available data from acquired cases suggests focal brain damage in language and sensorimotor brain networks, but little remains known about abnormal functional connectivity in idiopathic cases of FAS without structural damage. Here, connectomic analyses were completed on three patients with idiopathic FAS to investigate unique functional connectivity abnormalities underlying accent change for the first time. Machine learning (ML)-based algorithms generated personalized brain connectomes based on a validated parcellation scheme from the Human Connectome Project (HCP). Diffusion tractography was performed on each patient to rule out structural fiber damage to the language system. Resting-state-fMRI was assessed with ML-based software to examine functional connectivity between individual parcellations within language and sensorimotor networks and subcortical structures. Functional connectivity matrices were created and compared against a dataset of 200 healthy subjects to identify abnormally connected parcellations. Three female patients (28-42 years) who presented with accent changes from Australian English to Irish (n = 2) or American English to British English (n = 1) demonstrated fully intact language system structural connectivity. All patients demonstrated functional connectivity anomalies within language and sensorimotor networks in numerous left frontal regions and between subcortical structures in one patient. Few commonalities in functional connectivity anomalies were identified between all three patients, specifically 3 internal-network parcellation pairs. No common inter-network functional connectivity anomalies were identified between all patients. The current study demonstrates specific language, and sensorimotor functional connectivity abnormalities can exist and be quantitatively shown in the absence of structural damage for future study.

An agile, data-driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets.

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.

Real world demonstration of hand motor mapping using the structural connectivity atlas.

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.

Functional connectivity analysis of the depression connectome provides potential markers and targets for transcranial magnetic stimulation.

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.

Graph Theory Measures and Their Application to Neurosurgical Eloquence.

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.

Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis.

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.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response.

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.

A connectivity model of the anatomic substrates underlying Gerstmann syndrome.

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.

Parcellation-based tractographic modeling of the salience network through meta-analysis.

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.

Using a ResNet-18 Network to Detect Features of Alzheimer's Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on Odusami et al. Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071.

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

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.

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.

Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery.

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.

Interventional neurorehabilitation for promoting functional recovery post-craniotomy: a proof-of-concept.

The human brain is a highly plastic 'complex' network-it is highly resilient to damage and capable of self-reorganisation after a large perturbation. Clinically, neurological deficits secondary to iatrogenic injury have very few active treatments. New imaging and stimulation technologies, though, offer promising therapeutic avenues to accelerate post-operative recovery trajectories. In this study, we sought to establish the safety profile for 'interventional neurorehabilitation': connectome-based therapeutic brain stimulation to drive cortical reorganisation and promote functional recovery post-craniotomy. In n = 34 glioma patients who experienced post-operative motor or language deficits, we used connectomics to construct single-subject cortical networks. Based on their clinical and connectivity deficit, patients underwent network-specific transcranial magnetic stimulation (TMS) sessions daily over five consecutive days. Patients were then assessed for TMS-related side effects and improvements. 31/34 (91%) patients were successfully recruited and enrolled for TMS treatment within two weeks of glioma surgery. No seizures or serious complications occurred during TMS rehabilitation and 1-week post-stimulation. Transient headaches were reported in 4/31 patients but improved after a single session. No neurological worsening was observed while a clinically and statistically significant benefit was noted in 28/31 patients post-TMS. We present two clinical vignettes and a video demonstration of interventional neurorehabilitation. For the first time, we demonstrate the safety profile and ability to recruit, enroll, and complete TMS acutely post-craniotomy in a high seizure risk population. Given the lack of randomisation and controls in this study, prospective randomised sham-controlled stimulation trials are now warranted to establish the efficacy of interventional neurorehabilitation following craniotomy.

Delayed, Progressive Multivessel Occlusion After Resection of a Recurrent Glioma.

Glioblastoma multiforme (GBM) is one of the most common primary brain tumors with an aggressive natural history consistent with a median survival of less than two years. Most clinical research has primarily focused on improving overall survival through aggressive cytoreductive surgery and adjuvant radiochemotherapy. However, far less clinical guidance has been given for unexpected instances of neurologic decline following safe glioma resection in the setting of vascular etiology. Here, we report a 50-year-old man who presented to our clinic with a seizure. His preoperative magnetic resonance imaging (MRI) demonstrated a left hippocampal glioblastoma. Ten months following total resection, the patient presented again with rapid loss of vision and hemorrhagic papilledema. An MRI demonstrated a recurrence of his glioma, which was partially resected with no complications. Eight days after surgery, the patient suddenly became unresponsive and imaging revealed moderate blood in the resection cavity, which was evacuated in the operating room. Follow-up scans showed a posterior cerebral artery infarction, and two days later, a middle cerebral artery infarction, upon which care was withdrawn. We do not propose a mechanism by which this delayed ischemia occurred, especially as the middle cerebral artery was not damaged during surgery, however, we note that delayed ischemia may be one mechanism of damage following glioma resection, which should be studied further to improve patient outcomes.

Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex.

For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity-based parcellation approach that can be applied at the single-subject level. Utilizing normative diffusion data, we first developed a machine-learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient-specific maps independent of brain shape and pathological distortion. The supervised ML classifier re-parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re-parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas-based registration from healthy subjects by employing a voxel-wise connectivity approach based on individual data.

Anatomy and white-matter connections of the precuneus.

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.

Changes in the Brain Connectome Following Repetitive Transcranial Magnetic Stimulation for Stroke Rehabilitation.

Repetitive transcranial magnetic stimulation (rTMS) is a promising approach for post-stroke rehabilitation but there lacks a rationale strategy to plan, execute, and monitor treatment. We present a case of targeted rTMS using the Omniscient Infinitome software to devise targets for treatment in a post-stroke patient and describe the functional connectomic changes after treatment. A 19-year-old female with no medical history presented 19 months after suffering a left middle cerebral artery (MCA) superior division ischemic stroke, resulting in language impairment and diminished right upper extremity motor function. She underwent a resting-state MRI (rsMRI) with tractography and images were processed using the Omniscient Infinitome software. Analysis using the anomaly detection within the software enabled us to identify three targets for rTMS (left area 1, left area 45, and right area SFL). These areas were treated with 25 sessions of intermittent Theta Burst Stimulation (iTBS) over five days at 80% of motor threshold concomitantly with targeted physical therapy and speech therapy. At five months follow-up, her language and right upper extremity functions significantly improved. Her connectomic analysis revealed substantial neural changes, including normalization of the sensorimotor network, substantially thicker callosal fiber bundle connecting the two hemispheres, and increased cortical recruitment in her language network. We present the first description of robust connectomic alterations in a post-stroke patient following targeted rTMS treatment. Further studies on the use of rTMS with an emphasis on functional connectomics are warranted.