Parcellation-Based Connectivity Model of the Judgement Core.

Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translation. Forty-six task-based fMRI studies were used to generate activation-likelihood estimations (ALE) across moral, social, risky, and interpersonal judgement paradigms. Cortical parcellations overlapping these ALEs were used to delineate patterns in neurocognitive network engagement for the four judgement tasks. Moral judgement involved the bilateral superior frontal gyri, right temporal gyri, and left parietal lobe. Social judgement demonstrated a left-dominant frontoparietal network with engagement of right-sided temporal limbic regions. Moral and social judgement tasks evoked mutual engagement of the bilateral DMN. Both interpersonal and risk judgement were shown to involve a right-sided frontoparietal network with accompanying engagement of the left insular cortex, converging at the right-sided CEN. Cortical activation in normophysiological judgement function followed two separable patterns involving the large-scale neurocognitive networks. Specifically, the DMN was found to subserve judgement centered around social inferences and moral cognition, while the CEN subserved tasks involving probabilistic reasoning, risk estimation, and strategic contemplation.

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.

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.

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.

Host lysolipid differentially modulates virulence factor expression and antimicrobial susceptibility in Pseudomonas aeruginosa.

Lysophosphatidic acid (LPA) occurs naturally in inflammatory exudates and has previously been shown to increase the susceptibility of Pseudomonas aeruginosa to ß-lactam antibiotics whilst concomitantly reducing accumulation of the virulence factors pyoverdine and elastase. Here it is demonstrated that LPA can also exert inhibitory effects upon pyocyanin production in P. aeruginosa, as well as influencing susceptibility to a wide range of chemically diverse non ß-lactam antimicrobials. Most strikingly, LPA markedly antagonizes the effect of the polycationic antibiotics colistin and tobramycin at a concentration of 250 µg ml-1 whilst conversely enhancing their efficacy at the lower concentration of 8.65 µg ml-1, approximating the maximal physiological concentrations found in inflammatory exudates. Transcriptomic responses of the virulent strain UCBPP-PA14 to LPA were analysed using RNA-sequencing along with BioLog phenoarrays and whole cell assays in attempts to delineate possible mechanisms underlying these effects. The results strongly suggest involvement of LPA-induced carbon catabolite repression together with outer-membrane (OM) stress responses whilst raising questions about the effect of LPA upon other P. aeruginosa virulence factors including type III secretion. This could have clinical relevance as it suggests that endogenous LPA may, at concentrations found in vivo, differentially modulate antibiotic susceptibility of P. aeruginosa whilst simultaneously regulating expression of virulence factors, thereby influencing host-pathogen interactions during infection. The possibility of applying exogenous LPA locally as an enhancer of select antibiotics merits further investigation.

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.

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.

SMAD proteins: Mediators of diverse outcomes during infection.

Understanding the relationship between host and pathogen is key to combatting disease. SMAD transcription factors, which transmit TGF-ß superfamily signalling, mediate an array of outcomes during embryogenesis, inflammation, cancer, and immunity. Surprisingly, these activities can sometimes be directly opposed; for example, SMAD3 has been reported as tumour suppressor by arresting cell cycle progression but conversely promotes cancer metastasis. A growing body of literature has identified SMADs as prominent targets during viral and bacterial infection for modulating host signalling. During infection, the activity of SMAD-containing transcriptional complexes can be finely tuned by pathogens to enhance infectivity and spread. SMAD signalling can be modulated at many levels, such as upstream at the ligand and receptor, or by direct interactions with SMADs. These alterations can increase pathogen dissemination, induce fibrosis, over-activate, or attenuate the host immune response. Here, we summarise the diverse mechanisms by which pathogens have evolved to sway SMAD signalling in their favour. Understanding the intricacies of host-pathogen interactions through this lens may elucidate aspects of SMAD function in cancer development, homoeostasis, and immune signalling previously overlooked. These insights are an opportunity to identify novel TGF-ß or BMP-targeted therapeutics for applications to infectious disease contexts.

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.

LNK (SH2B3) inhibition expands healthy and Fanconi anemia human hematopoietic stem and progenitor cells.

Hematopoietic stem cell transplantation (HSCT) remains the only curative treatment for a variety of hematological diseases. Allogenic HSCT requires hematopoietic stem cells (HSCs) from matched donors and comes with cytotoxicity and mortality. Recent advances in genome modification of HSCs have demonstrated the possibility of using autologous HSCT-based gene therapy to alleviate hematologic symptoms in monogenic diseases, such as the inherited bone marrow failure (BMF) syndrome Fanconi anemia (FA). However, for FA and other BMF syndromes, insufficient HSC numbers with functional defects results in delayed hematopoietic recovery and increased risk of graft failure. We and others previously identified the adaptor protein LNK (SH2B3) as a critical negative regulator of murine HSC homeostasis. However, whether LNK controls human HSCs has not been studied. Here, we demonstrate that depletion of LNK via lentiviral expression of miR30-based short hairpin RNAs results in robust expansion of transplantable human HSCs that provided balanced multilineage reconstitution in primary and secondary mouse recipients. Importantly, LNK depletion enhances cytokine-mediated JAK/STAT activation in CD34+ hematopoietic stem and progenitor cells (HSPCs). Moreover, we demonstrate that LNK depletion expands primary HSPCs associated with FA. In xenotransplant, engraftment of FANCD2-depleted FA-like HSCs was markedly improved by LNK inhibition. Finally, targeting LNK in primary bone marrow HSPCs from FA patients enhanced their colony forming potential in vitro. Together, these results demonstrate the potential of targeting LNK to expand HSCs to improve HSCT and HSCT-based gene therapy.

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.

Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models.

PURPOSE: Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigating both the magnitude and directionality of various features on the classification into a positive or negative class. This manuscript presents a novel methodology, "Hollow-tree Super" (HOTS), designed to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, this methodology allows for accurate investigation of the directionality and magnitude various features have on classification and incorporates cross-validation to improve the accuracy and validity of the determined features of importance. METHODS: Using the Iris dataset, we first highlight the characteristics of HOTS by comparing it to other commonly used techniques for feature importance, including Gini Importance, Partial Dependence Plots, and Permutation Importance, and explain how HOTS resolves the weaknesses present in these three strategies for investigating feature importance. We then demonstrate how HOTS can be utilized in high dimensional spaces such as neuroscientific setting, by taking 60 Schizophrenic subjects from the publicly available SchizConnect database and applying the method to determine which regions of the brain were most important for the positive and negative classification of schizophrenia as determined by the positive and negative syndrome scale (PANSS). RESULTS: HOTS effectively replicated and supported the findings of feature importance for classification of the Iris dataset when compared to Gini importance, Partial Dependence Plots and Permutation importance, determining 'petal length' as the most important feature for positive and negative classification. When applied to the Schizconnect dataset, HOTS was able to resolve from 379 independent features, the top 10 most important features for classification, as well as their directionality for classification and magnitude compared to other features. Cross-validation supported that these same 10 features were consistently used in the decision-making process across multiple trees, and these features were localised primarily to the occipital and parietal cortices, commonly disturbed brain regions in those afflicted with Schizophrenia. CONCLUSION: HOTS effectively overcomes previous challenges of identifying feature importance at scale, and can be utilized across a swathe of disciplines. As computational power and data quantity continues to expand, it is imperative that a methodology is developed that is able to handle the demands of working with large datasets that contain a large number of features. This approach represents a unique way to investigate both the directionality and magnitude of feature importance when working at scale within a boosted tree model that can be easily visualized within commonly used software.

Using Quicktome for Intracerebral Surgery: Early Retrospective Study and Proof of Concept.

BACKGROUND: Neurosurgeons have limited tools in their armamentarium to visualize critical brain networks during surgical planning. Quicktome was designed using machine-learning to generate robust visualization of important brain networks that can be used with standard neuronavigation to minimize those deficits. We sought to see whether Quicktome could help localize important cerebral networks and tracts during intracerebral surgery. METHODS: We report on all patients who underwent keyhole intracranial surgery with available Quicktome-enabled neuronavigation. We retrospectively analyzed the locations of the lesions and determined functional networks at risks, including chief executive network, default mode network, salience, corticospinal/sensorimotor, language, neglect, and visual networks. We report on the postoperative neurologic outcomes of the patients and retrospectively determined whether the outcomes could be explained by Quicktome's functional localizations. RESULTS: Fifteen high-risk patients underwent craniotomies for intra-axial tumors, with the exception of one meningioma and one case of leukoencephalopathy. Eight patients were male. The median age was 49.6 years. Quicktome was readily integrated in our existing navigation system in every case. New postoperative neurologic deficits occurred in 8 patients. All new deficits, except for one resulting from a postoperative stroke, were expected and could be explained by preoperative findings by Quicktome. In addition, in those who did not have new neurologic deficits, Quicktome offered explanations for their outcomes. CONCLUSIONS: Quicktome helps to visualize complex functional connectomic networks and tracts by seamlessly integrating into existing neuronavigation platforms. The added information may assist in reducing neurological deficits and offer explanations for postsurgical outcomes.

The predictive value of PNH clones, 6p CN-LOH, and clonal TCR gene rearrangement for aplastic anemia diagnosis.

Acquired aplastic anemia (AA) is a life-threatening bone marrow aplasia caused by the autoimmune destruction of hematopoietic stem and progenitor cells. There are no existing diagnostic tests that definitively establish AA, and diagnosis is currently made via systematic exclusion of various alternative etiologies, including inherited bone marrow failure syndromes (IBMFSs). The exclusion of IBMFSs, which requires syndrome-specific functional and genetic testing, can substantially delay treatment. AA and IBMFSs can have mimicking clinical presentations, and their distinction has significant implications for treatment and family planning, making accurate and prompt diagnosis imperative to optimal patient outcomes. We hypothesized that AA could be distinguished from IBMFSs using 3 laboratory findings specific to the autoimmune pathogenesis of AA: paroxysmal nocturnal hemoglobinuria (PNH) clones, copy-number-neutral loss of heterozygosity in chromosome arm 6p (6p CN-LOH), and clonal T-cell receptor (TCR) γ gene (TRG) rearrangement. To test our hypothesis, we determined the prevalence of PNH, acquired 6p CN-LOH, and clonal TRG rearrangement in 454 consecutive pediatric and adult patients diagnosed with AA, IBMFSs, and other hematologic diseases. Our results indicated that PNH and acquired 6p CN-LOH clones encompassing HLA genes have ∽100% positive predictive value for AA, and they can facilitate diagnosis in approximately one-half of AA patients. In contrast, clonal TRG rearrangement is not specific for AA. Our analysis demonstrates that PNH and 6p CN-LOH clones effectively distinguish AA from IBMFSs, and both measures should be incorporated early in the diagnostic evaluation of suspected AA using the included Bayesian nomogram to inform clinical application.

Parcellation-based anatomic model of the semantic network.

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.

The role of rhizofiltration and allelopathy on the removal of cyanobacteria in a continuous flow system.

A continuous flow filtration system was designed to identify and quantify the removal mechanisms of Cyanobacteria (Microcystis aeruginosa) by hydroponic biofilters of Phalaris arundinacea compared to synthetic filters. The filtration units were continuously fed under plug-flow conditions with Microcystis grown in photobioreactors. Microcystis cells decreased at the two flow rates studied (1.2 ± 0.2 and 54 ± 3 cm3 min-1) and results suggested physical and chemical/biological removal mechanisms were involved. Physical interception and deposition was the main removal mechanism with packing density of the media driving the extent of cell removal at high flow, whilst physical and chemical/biological mechanisms were involved at low flow. At low flow, the biofilters decreased Microcystis cell numbers by 70% compared to the controls. The decrease in cell numbers in the biofilters was accompanied by a chlorotic process (loss of green colour), suggesting oxidative processes by the release of allelochemicals from the biofilters.