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SORL1 , the gene encoding the large multidomain SORLA protein, has emerged as only the fourth gene that when mutated can by itself cause Alzheimer’s disease (AD), and as a gene reliably linked to both the early- and late-onset forms of the disease. SORLA is known to interact with the endosomal trafficking regulatory complex called retromer in regulating the recycling of endosomal cargo, including the amyloid precursor protein (APP) and the glutamate receptor GluA1. Nevertheless, SORLA’s precise structural–functional relationship in endosomal recycling tubules remains unknown. Here, we address these outstanding questions by relying on crystallographic and artificial-intelligence evidence to generate a structural model for how SORLA folds and fits into retromer-positive endosomal tubules, where it is found to dimerize via both SORLA’s fibronectin-type-III (3Fn)- and VPS10p-domains. Moreover, we identify a SORLA fragment comprising the 3Fn-, transmembrane, and cytoplasmic domains that has the capacity to form a dimer, and to enhance retromer-dependent recycling of APP by decreasing its amyloidogenic processing. Collectively, these observations generate a model for how SORLA dimer (and possibly polymer) formation can function in stabilizing and enhancing retromer function at endosome tubules. These findings can inform investigation of the many AD-associated SORL1 variants for evidence of pathogenicity and can guide discovery of novel drugs for the disease.

To introduce a novel convolutional neural network (CNN)-based approach for frequency-and-phase correction (FPC) of MR spectroscopy (MRS) spectra to achieve fast and accurate FPC of single-voxel MEGA-PRESS MRS data.Two neural networks (one for frequency and one for phase) were trained and validated using published simulated and in vivo MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. The CNN-based approach was subsequently tested and compared to the current deep learning solution: multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance at varied signal-to-noise ratio (SNR) levels (i.e., 10, 5, and 2.5). Additional frequency and phase offsets (i.e., small, moderate, large) were also applied to the in vivo dataset, and the CNN model was compared to the conventional approach SR and model-based SR implementation (mSR).The CNN model is more robust to noise compared to the MLP-based approach due to having smaller mean absolute errors in both frequency (0.01 ± 0.01 Hz at SNR = 10 and 0.01 ± 0.02 Hz at SNR = 2.5) and phase (0.12 ± 0.09° at SNR = 10 and -0.07 ± 0.44° at SNR = 2.5) offset prediction. Furthermore, better performance was demonstrated for FPC when compared to the MLP-based approach, and SR when applied to the in vivo dataset for both with and without additional offsets.A CNN-based approach provides a solution to the automated preprocessing of MRS data, and the experimental results demonstrate the quantitatively improved spectra quality compared to the state-of-the-art approach.

This study examines the association between increased hippocampal glutamate and higher total positive symptom severity in converters to psychosis.

Species distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquitoAnopheles funestuscomprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants ofAn. funestusdiversification was impeded by lack of modern genomic resources. Here, we applied deep whole-genome sequencing and analysis to test the hypothesis that these two forms are ecotypes differentially adapted to breeding in natural swamps versus irrigated rice fields. We demonstrate genome-wide differentiation despite extensive microsympatry, synchronicity, and ongoing hybridization. Demographic inference supports a split only ~1,300 years ago, closely following the massive expansion of domesticated African rice cultivation ~1,850 years ago. Regions of highest divergence, concentrated in chromosomal inversions, were under selection during lineage splitting, consistent with local adaptation. The origin of nearly all variation implicated in adaptation, including chromosomal inversions, substantially predates the ecotype split, suggesting that rapid adaptation was fueled mainly by standing genetic variation. Sharp inversion frequency differences likely facilitated adaptive divergence between ecotypes, both by suppressing recombination between opposing chromosomal orientations of the two ecotypes, and by maximizing recombination within the structurally monomorphic rice ecotype. Our results align with growing evidence from diverse taxa that rapid ecological diversification can arise from evolutionarily old structural genetic variants that modify genetic recombination.Significance StatementLocal adaptation to heterogeneous environments is pervasive, but its underlying genetic basis is incompletely understood. Within a major African malaria vector,An. funestus, are two chromosomally differentiated groups that are co-localized, morphologically indistinguishable, and reported to differ both in ecology and behavior relevant to malaria transmission and control. Progress in understanding the genetic basis and environmental determinants of vector diversification was impeded by the lack of modern genomic resources. Here we perform deep whole-genome sequencing on individuals from these groups, establishing that they are differentiated genome-wide in a manner consistent with recent ecotype formation associated with the exploitation of a new anthropogenic larval habitat. Such rapid malaria vector diversification was facilitated by standing genetic variation, including evolutionarily old chromosomal rearrangements.

Alltägliche Vergesslichkeit - (k)ein Versagen unseres Gehirns? Schon wieder die Schlüssel verlegt, einen Termin verschwitzt, den Namen der neuen Kollegin vergessen? Wir alle kennen diese Momente von Vergesslichkeit, in denen unser Gehirn zu versagen scheint. Allerdings zeichnen neueste Erkenntnisse aus Psychologie, Neurobiologie, Medizin und Informatik ein anderes Bild: Das Vergessen ist kein Versagen unseres Verstandes; es ist nicht einmal eine harmlose Panne. Es ist vielmehr gut für unsere kognitiven und kreativen Fähigkeiten - eine notwendige Funktion, damit unser Gehirn optimal funktioniert. Von Studien über Bonobos in freier Wildbahn bis hin zum Besuch beim renommierten Experten für Entscheidungsfindung Daniel Kahneman - der Autor Scott A. Small blickt über die Grenzen der Disziplinen hinweg, um neue wissenschaftliche Erkenntnisse über das Gedächtnis in einen erhellenden Kontext zu stellen. Scott A. Small ist eine führende Autorität auf dem Gebiet des Gedächtnisses. Seine bahnbrechenden Beiträge wurden von der akademischen Welt mit verschiedenen Preisen gewürdigt. „Scott A. Small nutzt die modernen Erkenntnisse der -neurowissenschaftlichen Forschung, um das Vergessen als -einen sinnvollen - ja, überlebenswichtigen - Mechanismus zu beschreiben.“ Prof. Dr. Lutz Jäncke

The established causal genes in Alzheimer's disease (AD), APP, PSEN1, and PSEN2, are functionally characterized using biomarkers, capturing an in vivo profile reflecting the disease's initial preclinical phase. Mutations in SORL1, encoding the endosome recycling receptor SORLA, are found in 2%-3% of individuals with early-onset AD, and SORL1 haploinsufficiency appears to be causal for AD. To test whether SORL1 can function as an AD causal gene, we use CRISPR-Cas9-based gene editing to develop a model of SORL1 haploinsufficiency in Göttingen minipigs, taking advantage of porcine models for biomarker investigations. SORL1 haploinsufficiency in young adult minipigs is found to phenocopy the preclinical in vivo profile of AD observed with APP, PSEN1, and PSEN2, resulting in elevated levels of β-amyloid (Aβ) and tau preceding amyloid plaque formation and neurodegeneration, as observed in humans. Our study provides functional support for the theory that SORL1 haploinsufficiency leads to endosome cytopathology with biofluid hallmarks of autosomal dominant AD.

Considerable evidence has established the importance of specific nutrients that have been found vital for the developing brain. We hypothesize that in a similar manner there should be nutrients vital to the aging brain and that based on aging's distinct pathophysiology they should be different than those essential to development. Specific brain networks that govern cognition are particularly vulnerable to the aging process, resulting in what is referred to as 'cognitive aging'. Common late-life disorders, however, such as Alzheimer's disease also target these same brain networks. Studies have disambiguated cognitive aging from late-life disease by isolating regions and biological pathways within each network differentially linked to one or the other. This anatomical biology anchors a framework to identify nutrients and/or dietary bioactives relevant to cognitive aging whose utility is illustrated via a decades-long research program into how dietary bioactive flavanols benefit the brain. As we are living longer in cognitively more demanding lives, the framework's ultimate goal is to generate specific dietary recommendations that will fortify our mind for its golden years.

While impaired fear generalization is known to underlie a wide range of psychopathology, the extent to which exposure to trauma by itself results in deficient fear generalization and its neural abnormalities is yet to be studied. Similarly, the neural function of intact fear generalization in people who endured trauma and did not develop significant psychopathology is yet to be characterized. Here, we utilize a generalization fMRI task, and a network connectivity approach to clarify putative behavioral and neural markers of trauma and resilience. The generalization task enables longitudinal assessments of threat discrimination learning. Trauma-exposed participants (TE; N = 62), compared to healthy controls (HC; N = 26), show lower activity reduction in salience network (SN) and right executive control network (RECN) across the two sequential generalization stages, and worse discrimination learning in SN measured by linear deviation scores (LDS). Comparison of resilient, trauma-exposed healthy control participants (TEHC; N = 31), trauma exposed individuals presenting with psychopathology (TEPG; N = 31), and HC, reveals a resilience signature of network connectivity differences in the RECN during generalization learning measured by LDS. These findings may indicate a trauma exposure phenotype that has the potential to advance the development of innovative treatments by targeting and engaging specific neural dysfunction among trauma-exposed individuals, across different psychopathologies.

Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC's performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics. publishersversion published

The entorhinal cortex is subdivided into anterolateral entorhinal cortex (alERC) and posteromedial entorhinal cortex (pmERC) subregions, which are theorized to support distinct cognitive roles. This distinction is particularly important as the alERC is one of the earliest cortical regions affected by Alzheimer's pathology and related neurodegeneration. The relative associations of alERC/pmERC with neuropsychological test performance have not been examined. We examined how alERC/pmERC volumes differentially relate to performance on 1) the Modified Rey Auditory Learning Test (ModRey), a verbal memory test designed to assess normal/preclinical populations, 2) the Montreal Cognitive Assessment (MoCA), and 3) the National Alzheimer's Coordinating Center neuropsychological battery. We also examined whether alERC/pmERC volumes correlate with Alzheimer's disease cerebrospinal fluid (CSF) biomarkers. In 65 cognitively healthy (CDR = 0) older adults, alERC, but not pmERC, volume was associated with ModRey memory retention. Only alERC volume differentiated between participants who scored above and below the MoCA cutoff score for impairment. Evaluating the MoCA subdomains revealed that alERC was particularly associated with verbal recall. On the National Alzheimer's Coordinating Center battery, both alERC and pmERC volumes were associated with Craft story recall and Benson figure copy, but only alERC volume was associated with Craft story retention and semantic fluency. Neither alERC nor pmERC volume correlated with CSF levels of amyloid or tau, and regression analyses showed that alERC volume and CSF amyloid levels were independently associated with ModRey retention performance. Taken together, these results suggest that the alERC is important for memory performance and that alERC volume differences are related to a pattern of neuropsychological test performance (i.e., impairments in episodic memory and semantic fluency) typically seen in clinical Alzheimer's disease.

With the world's population aging, age-related memory decline is an impending cognitive epidemic. Assessing the impact of diet on cognitive aging, we conducted a controlled, randomized, parallel-arm dietary intervention with 211 healthy adults (50–75 years) investigating effects of either a placebo or 260, 510 and 770 mg/day of cocoa flavanols for 12-weeks followed by 8-weeks washout. The primary outcome was a newly-developed object-recognition task localized to the hippocampus’ dentate gyrus. Secondary outcomes included a hippocampal-dependent list-learning task and a prefrontal cortex-dependent list-sorting task. The alternative Healthy Eating Index and a biomarker of flavanol intake (gVLM) were measured. In an MRI substudy, hippocampal cerebral blood volume was mapped. Object-recognition and list-sorting performance did not correlate with baseline diet quality and did not improve after flavanol intake. However, the hippocampal-dependent list-learning performance was directly associated with baseline diet quality and improved after flavanol intake, particularly in participants in the bottom tertile of baseline diet quality. In the imaging substudy, a region-of-interest analysis was negative but a voxel-based-analysis suggested that dietary flavanols target the dentate gyrus. While replication is needed, these findings suggest that diet in general, and dietary flavanols in particular, may be associated with memory function of the aging hippocampus and normal cognitive decline.

This article discusses why glutamate levels are abnormally elevated in the hippocampus of patients with schizophrenia and related disorders.

Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale heterogeneous dataset (N = 10,158, age range 18 - 97) of structural brain MRIs in a healthy population from multiple publicly-available sources, upon which we train a deep learning model for brain age estimation. The availability of the large-scale dataset enables a more uniform age distribution across adult life-span for effective age estimation with no bias toward certain age groups. We demonstrate that the age estimation accuracy, evaluated with mean absolute error (MAE) and correlation coefficient (r), outperforms previously reported methods in both a hold-out test set reflective of the custom population (MAE = 4.06 years, r = 0.970) and an independent life-span evaluation dataset (MAE = 4.21 years, r = 0.960) on which a previous study has evaluated. We further demonstrate the utility of the estimated age in life-span aging analysis of cognitive functions. Furthermore, we conduct extensive ablation tests and employ feature-attribution techniques to analyze which regions contribute the most predictive value, demonstrating the prominence of the frontal lobe as well as pattern shift across life-span. In summary, we achieve age estimation performance comparable to previous studies but with a more heterogenous dataset confirming the efficacy of deep learning and the added utility of training with data both in larger number and more uniformly distributed than in previous studies. We demonstrate the regional contribution to our brain age predictions through multiple routes and confirm the association of divergence between estimated and chronological brain age with neuropsychological measures.

Background We examined neuroimaging-derived hippocampal biomarkers in subjects at clinical high risk (CHR) for psychosis to further characterize the pathophysiology of early psychosis. We hypothesized that glutamate hyperactivity, reflected by increased metabolic activity derived from functional magnetic resonance imaging in the CA1 hippocampal subregion and from proton magnetic resonance spectroscopy–derived hippocampal levels of glutamate/glutamine, represents early hippocampal dysfunction in CHR subjects and is predictive of conversion to syndromal psychosis. Methods We enrolled 75 CHR individuals with attenuated positive symptom psychosis-risk syndrome as defined by the Structured Interview for Psychosis-risk Syndromes. We used optimized magnetic resonance imaging techniques to measure 3 validated in vivo pathologies of hippocampal dysfunction—focal cerebral blood volume, focal atrophy, and evidence of elevated glutamate concentrations. All patients were imaged at baseline and were followed for up to 2 years to assess for conversion to psychosis. Results At baseline, compared with control subjects, CHR individuals had high glutamate/glutamine and elevated focal cerebral blood volume on functional magnetic resonance imaging, but only baseline focal hippocampal atrophy predicted progression to syndromal psychosis. Conclusions These findings provide evidence that CHR patients with attenuated psychotic symptoms have glutamatergic abnormalities, although only CHR patients who develop syndromal psychosis exhibit focal hippocampal atrophy. Furthermore, these results support the growing evidence that hippocampal dysfunction is an early feature of schizophrenia and related psychotic disorders.

Purpose: Lowering the sampling rate of accelerometers in physical activity research can dramatically increase study monitoring periods through longer battery life; however, the effect of reduced sampling rate on activity metric validity is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behavior types. Methods: Healthy adults wore sets of two Axivity AX3 accelerometers on the dominant wrist and hip for 24 hr. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate to vigorous activity, and behavioral activities were calculated and processed using both linear and nearest neighbor resampling. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results: Of the 54 total participants, 45 contributed >20 hr of hip wear time and 51 contributed >20 hr of wrist wear time. Strong correlation was observed between 25- and 100-Hz sampling rates in overall activity measurement (r = .97–.99), yet consistently lower activity was observed in data collected at 25 Hz (3.1%–13.9%). Reduced sleep and light activity and increased sedentary time was classified in 25-Hz data by machine learning models. Discrepancies were greater when linear interpolation resampling was used in postprocessing. Conclusions: The 25- and 100-Hz accelerometer data are highly correlated with predictable differences, which can be accounted for in interstudy comparisons. Sampling rate and resampling methods should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.

Investigating phase separation in neurodegeneration highlights evidence needed for causation

The few established causal genes in Alzheimer’s disease (AD), mutations in APP and PSENs, have been functionally characterized using biomarkers, capturing an in vivo profile reflecting the disease’s initial preclinical phase. SORL1, a gene encoding the endosome recycling receptor SORLA, epidemiologically behaves as a causal gene when truncating mutations lead to partial loss of protein function. Here, in an effort to test whether SORL1 can indeed function as an AD causal gene, we used CRISPR-Cas9-based gene editing to develop a novel model of SORL1 haploinsufficiency in Göttingen Minipigs taking advantage of porcine models for biomarker investigations. SORL1 haploinsufficiency in young minipigs was found to phenocopy the preclinical in vivo profile of AD observed with other causal genes, resulting in spinal fluid abnormalities in Aβ and tau, with no evident neurodegeneration or amyloid plaque formation. These studies provide functional support that SORL1 is a bona fide causal gene in AD, and when taken together with recent insight on other AD-causal genes, support the idea that dysfunctional endosomal recycling is a dominant pathogenic pathway in the disease.

We introduce DeepSPEC, a novel convolutional neural network (CNN) -based approach for frequency-and-phase correction (FPC) of MRS spectra to achieve fast and accurate FPC of single-voxel PRESS MRS and MEGA-PRESS data. In DeepSPEC, two neural networks, including one for frequency correction and one for phase correction were trained and validated using published simulated and in vivo PRESS and MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. DeepSPEC was subsequently tested and compared to the current deep learning solution - a “vanilla” neural network approach using multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance with noise at varied signal-to-noise (SNR) levels (i.e., 6 dB, 3 dB, and 1.5 dB). The testing showed that DeepSPEC is more robust to noise compared to the MLP-based approach due to having a smaller absolute error in both frequency and phase offset prediction. The DeepSPEC framework was capable of correcting frequency offset with 0.01±0.01 Hz and phase offset with 0.12±0.09° absolute errors on average for unseen simulated data at a high SNR (12 dB) and correcting frequency offset with 0.01±0.02 Hz and phase offset within -0.07±0.44° absolute errors on average at very low SNR (1.5 dB). Furthermore, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and DeepSPEC demonstrated better performance for FPC when compared to the MLP-based approach. Results also show DeepSPEC has superior performance than the model-based SR implementation (mSR) in FPC by having higher accuracy in a wider range of additional offsets. These results represent a proof of concept for the use of CNNs for preprocessing MRS data and demonstrate that DeepSPEC accurately predicts frequency and phase offsets at varying noise levels with state-of-the-art performance.

Scientific progress in understanding human disease can be measured by the effectiveness of its treatment. Antipsychotic drugs have been proven to alleviate acute psychotic symptoms and prevent their recurrence in schizophrenia, but the outcomes of most patients historically have been suboptimal. However, a series of findings in studies of first-episode schizophrenia patients transformed the psychiatric field's thinking about the pathophysiology, course, and potential for disease-modifying effects of treatment. These include the relationship between the duration of untreated psychotic symptoms and outcome; the superior responses of first-episode patients to antipsychotics compared with patients with chronic illness, and the reduction in brain gray matter volume over the course of the illness. Studies of the effectiveness of early detection and intervention models of care have provided encouraging but inconclusive results in limiting the morbidity and modifying the course of illness. Nevertheless, first-episode psychosis studies have established an evidentiary basis for considering a team-based, coordinated specialty approach as the standard of care for treating early psychosis, which has led to their global proliferation. In contrast, while clinical high-risk research has developed an evidence-based care model for decreasing the burden of attenuated symptoms, no treatment has been shown to reduce risk or prevent the transition to syndromal psychosis. Moreover, the current diagnostic criteria for clinical high risk lack adequate specificity for clinical application. What limits our ability to realize the potential of early detection and intervention models of care are the lack of sensitive and specific diagnostic criteria for pre-syndromal schizophrenia, validated biomarkers, and proven therapeutic strategies. Future research requires methodologically rigorous studies in large patient samples, across multiple sites, that ideally are guided by scientifically credible pathophysiological theories for which there is compelling evidence. These caveats notwithstanding, we can reasonably expect future studies to build on the research of the past four decades to advance our knowledge and enable this game-changing model of care to become a reality.

The neuropathological hallmarks of Alzheimer's disease include amyloid plaques and neurofibrillary tangles. Tau pathology correlates well with impaired neuronal activity and dementia. Focused ultrasound coupled with systemic administration of microbubbles has previously been shown to open the blood-brain barrier and induce an immune response, which, in an amyloid AD mouse model, resulted in the reduction of the amyloid brain load. Methods: In this study, we investigated the effect of focused ultrasound at the early stages of tau pathology (pre-tangle) in the rTg4510 mouse model. Results: Reduction of phosphorylated tau from the hippocampal formation processes, and particularly the pyramidal CA1 neurons, was noted in the ultrasound-treated brains without an associated increase in the phosphorylated tau-affected cell somas, typically associated with disease progression. Attenuation of the pathology was found to correlate well with the ultrasound-initiated immune response without compromising neuronal integrity. Unilateral ultrasound application resulted in a bilateral effect indicating a broader reduction of the phosphorylated tau. Conclusion: Findings presented herein reinforce the premise of ultrasound in reducing tau pathology and thus curbing the progression of Alzheimer's disease.

Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made in classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD's neuroimaging signatures., Published in IEEE ISBI 2021. Available at https://ieeexplore.ieee.org/document/9433808

Knowledge of species ages and their distribution enhance our understanding of processes that create and maintain species diversity at both local and regional levels. The largest family of freshwater mussels (Unionidae), reach their highest species diversity in drainages of the southeastern united states. By sequencing multiple loci from mussel species distributed throughout the drainages in this region, we attempt to uncover historical patterns of divergence and determine the role of vicariance events on the species formation in mussels and extend our hypothesis to freshwater animals in general. We analyzed 346 sequences from five genera encompassing 37 species. Species were sampled across 12 distinct drainages ending either in the Atlantic Ocean or the Gulf of Mexico. Overall the topologies of the different genera returned phylogenetic trees that were congruent with geographically contiguous drainages. The most common pattern was the grouping between the Atlantic slope and gulf coast drainages, however the Tennessee drainage was often the exception to this pattern grouping with the Atlantic slope. Most mussel species find a most recent common ancestor within a drainage before finding an ancestor between drainages. This supports the hypothesis of allopatric divergence followed by later burst of speciation within a drainage. Our estimated divergence times for the Atlantic-Gulf split agree with other studies estimating vicariance in fish species of the Atlantic and gulf coast.

The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods.First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer's dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration.The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aβ = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology.The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.

The few established causal genes in Alzheimer’s disease (AD), mutations in APP and PSENs, have been functionally characterized using biomarkers, capturing an in vivo profile reflecting the disease’s initial preclinical phase. SORL1, a gene encoding the endosome recycling receptor SORLA, epidemiologically behaves as a causal gene when truncating mutations lead to partial loss of protein function. Here, in an effort to test whether SORL1 can indeed function as an AD causal gene, we used CRISPR-Cas9-based gene editing to develop a novel model of SORL1 haploinsufficiency in Gottingen Minipigs taking advantage of porcine models for biomarker investigations. SORL1 haploinsufficiency in young minipigs was found to phenocopy the preclinical in vivo profile of AD observed with other causal genes, resulting in spinal fluid abnormalities in Ab and tau, with no evident neurodegeneration or amyloid plaque formation. These studies provide functional support that SORL1 is a bona fide causal gene in AD, and when taken together with recent insight on other AD-causal genes, support the idea that dysfunctional endosomal recycling is a dominant pathogenic pathway in the disease.

Significance Introgressive hybridization is prevalent in recent and rapid animal radiations, and emerging evidence suggests that it leads to the sharing of genetic variation that can facilitate adaptation to new environments and generate novel phenotypes. Here we study a recent and rapid radiation of African mosquitoes in which only one species, An. funestus, is a primary human malaria vector with a continent-wide geographic distribution. We trace the evolutionary history of the group and demonstrate introgression events between multiple species, the most recent of which involved substantial gene flow into An. funestus that preceded its range expansion across tropical Africa. Our findings point to introgression as an underappreciated factor contributing to the acquisition of high malaria vectorial capacity., Advances in genomics have led to an appreciation that introgression is common, but its evolutionary consequences are poorly understood. In recent species radiations the sharing of genetic variation across porous species boundaries can facilitate adaptation to new environments and generate novel phenotypes, which may contribute to further diversification. Most Anopheles mosquito species that are of major importance as human malaria vectors have evolved within recent and rapid radiations of largely nonvector species. Here, we focus on one of the most medically important yet understudied anopheline radiations, the Afrotropical Anopheles funestus complex (AFC), to investigate the role of introgression in its diversification and the possible link between introgression and vector potential. The AFC comprises at least seven morphologically similar species, yet only An. funestus sensu stricto is a highly efficient malaria vector with a pan-African distribution. Based on de novo genome assemblies and additional whole-genome resequencing, we use phylogenomic and population genomic analyses to establish species relationships. We show that extensive interspecific gene flow involving multiple species pairs has shaped the evolutionary history of the AFC since its diversification. The most recent introgression event involved a massive and asymmetrical movement of genes from a distantly related AFC lineage into An. funestus, an event that predated and plausibly facilitated its subsequent dramatic geographic range expansion across most of tropical Africa. We propose that introgression may be a common mechanism facilitating adaptation to new environments and enhancing vectorial capacity in Anopheles mosquitoes.

A hub-and-spoke model with endosomal recycling as the hub can reconcile the pathogenic contribution of amyloid precursor protein to Alzheimer’s disease.