Search

Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective study, patients who had undergone a computed tomography (CT) scan within 3 months before surgery were included. We randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features out of 1,288 radiomic features to classify malignant renal tumors. Results: The training set included 396 patients, and the test set included 103 patients. The percentage of extracted radiomic features from patients was 32% (385/1218) after the reproducibility test. In terms of the average Area Under the Receiver Operating Characteristic Curve (AU-ROC) and the average Area Under the Precision-Recall Curve (AU-PRC), the Random Forest model achieved better performance (AU-ROC = 0.725; AU-PRC = 0.899). An average accuracy of 0.778 was obtained on evaluation with the hold-out test set. At the optimal threshold, the Random Forest model showed an F1 score of 0.746, precision of 0.862, sensitivity of 0.657, specificity of 0.651, and Negative Predictive Value (NPV) of 0.364. Conclusions: Our machine learning-based CT radiomics classification model performed well for the independent test set, indicating that it could be a useful tool for discriminating between malignant and benign solid renal tumors. [ABSTRACT FROM AUTHOR]

Background: Causal explanations for the association of young motherhood with increased risk for child attention‐deficit hyperactivity disorder (ADHD) remain unclear. Methods: The ABCD Study recruited 11,878 youth from 22 sites across the United States between June 1, 2016 and October 15, 2018. This cross‐sectional analysis of 8,514 children aged 8–11 years excluded 2,260 twins/triplets, 265 adopted children, and 839 younger siblings. We examined associations of maternal age with ADHD clinical range diagnoses based on the Child Behavior Checklist and NIH Toolbox Flanker Attention Scores using mixed logistic and linear regression models, respectively. We conducted confounding and causal mediation analyses using genotype array, demographic, socioeconomic, and prenatal environment data to investigate which genetic and environmental variables may explain the association between young maternal age and child ADHD. Results: In crude models, each 10‐year increase in maternal age was associated with 32% decreased odds of ADHD clinical range diagnosis (OR = 0.68; 95% CI [0.59, 0.78]) and 1.09‐points increased NIH Flanker Attention Scores (β = 1.09; 95% CI [0.76, 1.41]), indicating better child visual selective attention. However, adjustment for confounders weakened these associations. The strongest confounders were family income, caregiver education, and ADHD polygenic risk score for ADHD clinical range diagnoses, and family income, caregiver education, and race/ethnicity for NIH Flanker Attention Scores. Breastfeeding duration, prenatal alcohol exposure, and prenatal tobacco exposure were responsible for up to 18%, 6%, and 4% mediation, respectively. Conclusions: Socioeconomic disadvantages were likely the primary explanation for the association of young maternal age with child ADHD, although genetics and modifiable environmental factors also played a role. Public policies aimed at reducing the burden of ADHD associated with young motherhood should target socioeconomic inequalities and support young pregnant women by advocating for reduced prenatal tobacco exposure and healthy breastfeeding practices after childbirth. [ABSTRACT FROM AUTHOR]

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9–10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC–AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (pfdr <.001, ηp2 =.011–.034; adjusted for covariates) and higher cognitive genome‐wide polygenic scores (GPSs) (pfdr <.001, ηp2 <.005). Structural equation models revealed that the GPS‐intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects =.006–.009; p =.002–.022; sex‐stratified analysis). The finding of the sex modulatory effect on the gene–brain–cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence. [ABSTRACT FROM AUTHOR]

The genetic protective factors for cognitive decline in aging remain unknown. Predicting an individual's rate of cognitive decline—or with better cognitive resilience—using genetics will allow personalized intervention for cognitive enhancement and the optimal selection of target samples in clinical trials. Here, using genome-wide polygenic scores (GPS) of cognitive capacity as the genomic indicators for variations of human intelligence, we analyzed the 18-year records of cognitive and behavioral data of 8511 European-ancestry adults from the Wisconsin Longitudinal Study (WLS), specifically focusing on the cognitive assessments that were repeatedly administered to the participants with their average ages of 64.5 and 71.5. We identified a significant interaction effect between age and cognitive capacity GPS, which indicated that a higher cognitive capacity GPS significantly correlated with a slower cognitive decline in the domain of immediate memory recall (β = 1.86 × 10−1, p-value = 1.79 × 10−3). The additional phenome-wide analyses identified several associations between cognitive capacity GPSs and cognitive/behavioral phenotypes, such as similarities task (β = 1.36, 95% CI = (1.22, 1.51), p-value = 3.59 × 10−74), number series task (β = 0.94, 95% CI = (0.85, 1.04), p-value = 2.55 × 10−78), IQ scores (β = 1.42, 95% CI = (1.32, 1.51), p-value = 7.74 × 10−179), high school classrank (β = 1.86, 95% CI = (1.69, 2.02), p-value = 3.07 × 10−101), Openness from the BIG 5 personality factor (p-value = 2.19 × 10−14, β = 0.57, 95% CI = (0.42, 0.71)), and leisure activity of reading books (β = 0.50, 95% CI = (0.40, 0.60), p-value = 2.03 × 10−21), attending cultural events, such as concerts, plays, or museums (β = 0.60, 95% CI = (0.49, 0.72), p-value = 2.06 × 10−23), and watching TV (β = −0.48, 95% CI = (−0.59, −0.37), p-value = 4.16 × 10−18). As the first phenome-wide analysis of cognitive and behavioral phenotypes, this study presents the novel genetic protective effects of cognitive ability on the decline of memory recall in an aging population. [ABSTRACT FROM AUTHOR]

Objective: Post-stroke cognitive impairment (PSCI) is resistant to treatment. Recent studies have widely applied repetitive transcranial magnetic stimulation (rTMS) to treat various brain dysfunctions, such as post-stroke syndromes. Nonetheless, a protocol for PSCI has not been established. Therefore, this study is aimed to evaluate the therapeutic effect of our high-frequency rTMS protocol for PSCI during the chronic phase of stroke. Methods: In this prospective study, ten patients with PSCI were enrolled and received high-frequency rTMS on the ipsilesional dorsolateral prefrontal cortex (DLPFC) for 10 sessions (5 days per week for 2 weeks). Cognitive and affective abilities were assessed at baseline and 2 and 14 weeks after rTMS initiation. To investigate the therapeutic mechanism of rTMS, the mRNA levels of pro-inflammatory cytokines (interleukin (IL)-6, IL-1β, transforming growth factor beta [TGF-β], and tumor necrosis factor alpha [TNF-α]) in peripheral blood samples were quantified using reverse transcription polymerase chain reaction, and cognitive functional magnetic resonance imaging (fMRI) was conducted at baseline and 14 weeks in two randomly selected patients after rTMS treatment. Results: The scores of several cognitive evaluations, i.e., the Intelligence Quotient (IQ) of Wechsler Adult Intelligence Scale, auditory verbal learning test (AVLT), and complex figure copy test (CFT), were increased after completion of the rTMS session. After 3 months, these improvements were sustained, and scores on the Mini-Mental Status Examination and Montreal Cognitive Assessment (MoCA) were also increased (p < 0.05). While the Geriatric Depression Scale (GeDS) did not show change among all patients, those with moderate-to-severe depression showed amelioration of the score, with marginal significance. Expression of pro-inflammatory cytokines was decreased immediately after the ten treatment sessions, among which, IL-1β remained at a lower level after 3 months. Furthermore, strong correlations between the decrease in IL-6 and increments in AVLT (r = 0.928) and CFT (r = 0.886) were found immediately after the rTMS treatment (p < 0.05). Follow-up fMRI revealed significant activation in several brain regions, such as the medial frontal lobe, hippocampus, and angular area. Conclusions: High-frequency rTMS on the ipsilesional DLPFC may exert immediate efficacy on cognition with the anti-inflammatory response and changes in brain network in PSCI, lasting at least 3 months. [ABSTRACT FROM AUTHOR]

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held‐out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr =.012) and lower functioning on the Children's Global Assessment Scale (pcorr =.012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr =.008). Brain age prediction was more accurate using ComBat‐harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth. [ABSTRACT FROM AUTHOR]

Background: Cognitive behavioral therapy (CBT) is an effective, first‐line treatment for pediatric obsessive‐compulsive disorder (OCD). While neural predictors of treatment outcomes have been identified in adults with OCD, robust predictors are lacking for pediatric patients. Herein, we sought to identify brain structural markers of CBT response in youth with OCD. Methods: Twenty‐eight children/adolescents with OCD and 27 matched healthy participants (7‐ to 18‐year‐olds, M = 11.71 years, SD = 3.29) completed high‐resolution structural and diffusion MRI (all unmedicated at time of scanning). Patients with OCD then completed 12–16 sessions of CBT. Subcortical volume and cortical thickness were estimated using FreeSurfer. Structural connectivity (streamline counts) was estimated using MRtrix. Results: Thinner cortex in nine frontoparietal regions significantly predicted improvement in Children's Yale‐Brown Obsessive‐Compulsive Scale (CY‐BOCS) scores (all ts > 3.4, FDR‐corrected ps <.05). These included middle and superior frontal, angular, lingual, precentral, superior temporal, and supramarginal gyri (SMG). Vertex‐wise analyses confirmed a significant left SMG cluster, showing large effect size (Cohen's d = 1.42) with 72.22% specificity and 90.00% sensitivity in predicting CBT response. Ten structural connections between cingulo‐opercular regions exhibited fewer streamline counts in OCD (all ts > 3.12, Cohen's ds > 0.92) compared with healthy participants. These connections predicted post‐treatment CY‐BOCS scores, beyond pretreatment severity and demographics, though not above and beyond cortical thickness. Conclusions: The current study identified group differences in structural connectivity (reduced among cingulo‐opercular regions) and cortical thickness predictors of CBT response (thinner frontoparietal cortices) in unmedicated children/adolescents with OCD. These data suggest, for the first time, that cortical and white matter features of task control circuits may be useful in identifying which pediatric patients respond best to individual CBT. [ABSTRACT FROM AUTHOR]

Social behavior is transmitted cross-generationally through coordinated behavior within attachment bonds. Parental depression and poor parental care are major risks for disruptions of such coordination and are associated with offspring's psychopathology and interpersonal dysfunction. Given the key role of the cortico-basal ganglia (CBG) circuits in social communication, we examined similarities (concordance) of parent–offspring CBG white matter (WM) connections and how parental history of major depressive disorder (MDD) and early parental care moderate these similarities. We imaged 44 parent–offspring dyads and investigated WM connections between basal-ganglia seeds and selected regions in temporal cortex using diffusion tensor imaging (DTI) tractography. We found significant concordance in parent–offspring strength of CBG WM connections, moderated by parental lifetime-MDD and care. The results showed diminished neural concordance among dyads with a depressed parent and that better parental care predicted greater concordance, which also provided a protective buffer against attenuated concordance among dyads with a depressed parent. Our findings provide the first neurobiological evidence of concordance between parents-offspring in WM tracts and that concordance is diminished in families where parents have lifetime-MDD. This disruption may be a risk factor for intergenerational transmission of psychopathology. Findings emphasize the long-term role of early caregiving in shaping the neural concordance among at-risk and affected dyads. [ABSTRACT FROM AUTHOR]

Summary: Objective: Noncoding alleles of the fat mass and obesity‐associated (FTO) gene have been associated with obesity risk, yet the underlying mechanisms remain unknown. Risk allele carriers show alterations in brain structure and function, but previous studies have not disassociated the effects of genotype from those of body mass index (BMI). Methods: Differences in brain structure and function were examined in children without obesity grouped by their number of copies (0,1,2) of the FTO obesity‐risk single‐nucleotide polymorphism (SNP) rs1421085. One hundred five 5‐ to 10‐year‐olds (5th–95th percentile body fat) were eligible to participate. Usable scans were obtained from 93 participants (15 CC [homozygous risk], 31 CT [heterozygous] and 47 TT [homozygous low risk]). Results: Homozygous C allele carriers (CCs) showed greater grey matter volume in the cerebellum and temporal fusiform gyrus. CCs also demonstrated increased bilateral cerebellar white matter fibre density and increased resting‐state functional connectivity between the bilateral cerebellum and regions in the frontotemporal cortices. Conclusions: This is the first study to examine brain structure and function related to FTO alleles in young children not yet manifesting obesity. This study lends support to the notion that the cerebellum may be involved in FTO‐related risk for obesity, yet replication and further longitudinal study are required. [ABSTRACT FROM AUTHOR]

Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction. [ABSTRACT FROM AUTHOR]

Anorexia nervosa (AN) is a debilitating illness and existing interventions are only modestly effective. This study aimed to determine whether AN pathophysiology is associated with altered connections within fronto-accumbal circuitry subserving reward processing. Diffusion and resting-state functional MRI scans were collected in female inpatients with AN ( n = 22) and healthy controls (HC; n = 18) between the ages of 16 and 25 years. Individuals with AN were scanned during the acute, underweight phase of the illness and again following inpatient weight restoration. HC were scanned twice over the same timeframe. Based on univariate and multivariate analyses of fronto-accumbal circuitry, underweight individuals with AN were found to have increased structural connectivity (diffusion probabilistic tractography), increased white matter anisotropy (tract-based spatial statistics), increased functional connectivity (seed-based correlation in resting-state fMRI), and altered effective connectivity (spectral dynamic causal modeling). Following weight restoration, fronto-accumbal structural connectivity continued to be abnormally increased bilaterally with large (partial η2 = 0.387; right NAcc-OFC) and moderate (partial η2 = 0.197; left NAcc-OFC) effect sizes. Increased structural connectivity within fronto-accumbal circuitry in the underweight state correlated with severity of eating disorder symptoms. Taken together, the findings from this longitudinal, multimodal neuroimaging study offer converging evidence of atypical fronto-accumbal circuitry in AN. Hum Brain Mapp 37:3835-3846, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Given the high prevalence rates of comorbidity of anxiety and depressive disorders, identifying a common neural pathway to both disorders is important not only for better diagnosis and treatment, but also for a more complete conceptualization of each disease. Hippocampal abnormalities have been implicated in anxiety and depression, separately; however, it remains unknown whether these abnormalities are also implicated in their comorbidity. Here we address this question by testing 32 adults with generalized anxiety disorder (15 GAD only and 17 comorbid MDD) and 25 healthy controls (HC) using multimodal MRI (structure, diffusion and functional) and automated hippocampal segmentation. We demonstrate that (i) abnormal microstructure of the CA1 and CA2-3 is associated with GAD/MDD comorbidity and (ii) decreased anterior hippocampal reactivity in response to repetition of the threat cue is associated with GAD (with or without MDD comorbidity). In addition, mediation-structural equation modeling (SEM) reveals that our hippocampal and dimensional symptom data are best explained by a model describing a significant influence of abnormal hippocampal microstructure on both anxiety and depression-mediated through its impact on abnormal hippocampal threat processing. Collectively, our findings show a strong association between changes in hippocampal microstructure and threat processing, which together may present a common neural pathway to comorbidity of anxiety and depression. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Clinical anxiety is associated with generalization of conditioned fear, in which innocuous stimuli elicit alarm. Using Pavlovian fear conditioning (electric shock), we quantify generalization as the degree to which subjects' neurobiological responses track perceptual similarity gradients to a conditioned stimulus. Previous studies show that the ventro medial prefrontal cortex (vmPFC) inversely and ventraltegmental are a directly track the gradient of perceptual similarity to the conditioned stimulus in healthy individuals, whereas clinically anxious individuals fail to discriminate. Here, we extend this work by identifying specific functional roles within the prefrontal-limbic circuit. We analyzed fMRI time-series acquired from 57 human subjects during a fear generalization task using entropic measures of circuit-wide regulation and feedback (power spectrumscale invariance/autocorrelation), incombinationwith structural (diffusion MRI-probabilistic tractography)andfunctional (stochastic dynamic causal modeling) measures of prefrontal-limbic connectivity within the circuit. Group comparison and correlations with anxiety severity across 57 subjects revealed dysregulatory dynamic signatures within the inferior frontal gyrus (IFG), which our prior work has linked to impaired feedback within the circuit. Bayesian model selection then identified a fully connected prefrontal-limbic model comprising the IFG, vmPFC, and amygdala. Dysregulatory IFG dynamics were associated with weaker reciprocal excitatory connectivity between the IFG and the vmPFC. The vmPFC exhibited inhibitory influence on the amygdala. Our current results, combined with our previous work across a threatperception spectrum of 137 subjects and a meta-analysis of 366 fMRI studies, dissociate distinct roles for three prefrontal-limbic regions, wherein the IFG provides evaluation of stimulus meaning, which then informs the vmPFC in inhibiting the amygdala. [ABSTRACT FROM AUTHOR]

Aggression is widely observed in children with attention deficit/hyperactivity disorder (ADHD) and has been frequently linked to frustration or the unsatisfied anticipation of reward. Although animal studies and human functional neuroimaging implicate altered reward processing in aggressive behaviors, no previous studies have documented the relationship between fronto-accumbal circuitry-a critical cortical pathway to subcortical limbic regions-and aggression in medication-naive children with ADHD. To address this, we collected behavioral measures and parental reports of aggression and impulsivity, as well as structural and diffusion MRI, from 30 children with ADHD and 31 healthy controls (HC) (mean age, 10±2.1 SD). Using grey matter morphometry and probabilistic tractography combined with multivariate statistical modeling (partial least squares regression and support vector regression), we identified anomalies within the fronto-accumbal circuit in childhood ADHD, which were associated with increased aggression. More specifically, children with ADHD showed reduced right accumbal volumes and frontal-accumbal white matter connectivity compared with HC. The magnitude of the accumbal volume reductions within the ADHD group was significantly correlated with increased aggression, an effect mediated by the relationship between the accumbal volume and impulsivity. Furthermore, aggression, but not impulsivity, was significantly explained by multivariate measures of fronto-accumbal white matter connectivity and cortical thickness within the orbitofrontal cortex. Our multi-modal imaging, combined with multivariate statistical modeling, indicates that the fronto-accumbal circuit is an important substrate of aggression in children with ADHD. These findings suggest that strategies aimed at probing the fronto-accumbal circuit may be beneficial for the treatment of aggressive behaviors in childhood ADHD. [ABSTRACT FROM AUTHOR]

Abstract: In the rabbit retina, there are two types of horizontal cell (HC). The axonless A-type HCs form a coupled network via connexin 50 (Cx50) gap junctions in the outer plexiform layer (OPL). The axon-bearing B-type HCs form two independently coupled networks; the dendritic network via gap junctions consisted of unknown Cx and the axon terminal network via Cx57. The present study was conducted to examine the localization and morphological features of Cx50 and Cx57 gap junctions in rabbit HCs at cellular and subcellular levels. The results showed that each gap junction composed of Cx50 or Cx57 showed distinct features. The larger Cx50 gap junctions were located more proximally than the smaller Cx50 gap junctions. Both Cx50 plaques formed symmetrical homotypic gap junctions, but some small ones had an asymmetrical appearance, suggesting the presence of heterotypic gap junctions or hemichannels. In contrast, Cx57 gap junctions were found in the more distal part of the OPL but never on the axon terminal endings entering the rod spherules, and they were exclusively homotypic. Interestingly, about half of the Cx57 gap junctions appeared to be invaginated. These distinct features of Cx50 and Cx57 gap junctions show the variety of HC gap junctions and may provide insights into the function of different types of HCs. [Copyright &y& Elsevier]

Background: Neuropsychiatric disturbances, such as depression and anxiety, are observed in 90% of Alzheimer's disease (AD) patients and are frequent in those at risk for AD (Lyketsos CG, et al. 2012). Similarly, epidemiological studies show that neurodegeneration and clinical symptoms occur more rapidly in females once diagnosed. In addition to cognitive decline and daily anxiety, AD patients (∼50%) become confused, agitated, and anxious in the evening, often referred to as "sundowning"(Canevelli M, et al. 2016). This circadian disruption can also greatly impact AD pathology. Restlessness before and during sleep has been shown to inhibit Ab clearance, increase neurofibrillary tangles, and dysregulate synaptic activity, all of which are characteristic hallmarks of AD (Cordone S, et al. 2019). Method: Using our activity‐dependent tagging system, the ArcCreERT2 x (ChR2)‐EYFP x AD (APP/PS1), we labeled neurons activated during learning. These neurons can then can be compared with secondary neuronal ensembles activated during memory retrieval. The neurons activated at both time points represent an engram. Here, we aimed to identify the neural ensembles linking anxiety and memory loss following AD progression by utilizing behavioral studies, calcium imaging and whole‐brain microscopy, in female and male mice. Result: We found: 1) Female AD mice exhibited anxiety‐like behavior at an earlier age compared to controls and male mice, 2) AD female mice displayed memory deficits as early as 2 months of age, 3) Anxiety‐like behavior correlated with memory impairment only in AD female mice, 4) Unlike their male counterparts, female AD mice showed a decline in memory traces in the CA3 of the hippocampus, and 5) Male AD mice exhibit sundowning behavior at 2 months which correlates to increased calcium transients in the vCA1. We have also translated these findings to humans using the ADNI dataset. In humans, anxiety predicts transition to dementia and that anxiety has a sex‐specific effect on brain atrophy. Conclusion: To use anxiety as a neuropsychiatric biomarker of AD in the human population in combination with current imaging and cognitive testing and to find novel brain areas associated with increased anxiety and memory loss. This could provide new therapeutic targets for those at risk for AD. [ABSTRACT FROM AUTHOR]

Background: Neuropsychiatric disturbances, such as depression and anxiety, are observed in 90% of Alzheimer's disease (AD) patients and are frequent in those at risk for AD. However, until recently, much of the research narrowly targeted co‐morbid depression. Clinical reports have provided evidence that anxiety symptoms predict the conversion to AD, over and beyond the effects of depression, memory loss, and even atrophy. Similarly, epidemiological studies show that neurodegeneration and clinical symptoms occur more rapidly in females once diagnosed. Although most AD studies have been performed using male mice, females represent two‐thirds of the AD population and are more susceptible to depression and anxiety. Method: To study how anatomical sex and anxiety impact AD progression, I used our activity‐dependent tagging system mice. These mice allow for brain‐wide indelible labeling of neurons activated during learning, which then can be compared with secondary neuronal ensembles activated during memory retrieval. The neurons activated at both time points represent a memory trace or engram. Here, we aimed to identify the neural ensembles linking anxiety and memory loss following AD progression by utilizing behavioral studies, calcium imaging and whole‐brain microscopy, in female and male mice. Result: We found: 1) Female AD mice exhibited anxiety‐like behavior at an earlier age compared to controls and male mice, 2) AD female mice displayed memory deficits as early as 2 months of age, 3) Anxiety‐like behavior correlated with memory impairment only in AD female mice, and 4) Unlike their male counterparts, female AD mice showed a decline in memory traces in the CA3 of the hippocampus. Conclusion: We now believe that anxiety could represent a neuropsychiatric predictor of AD and are currently working to translate these findings to humans using the Alzheimer's disease neuroimaging initiative (ADNI) dataset. We have found that in humans, anxiety predicts transition to dementia and that anxiety has a sex‐specific effect on brain atrophy. [ABSTRACT FROM AUTHOR]