Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.

BACKGROUND: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE: We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS: Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS: Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS: Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.

[Successful in-hospital clamshell thoracotomy in a young male patient with polytrauma (ISS 57) : A case report]. / Erfolgreiche innerklinische Clamshell-Thorakotomie bei einem jungen polytraumatisierten (ISS 57) Patienten : Ein Fallbericht.

Life-threatened injured patients who suffer a cardiovascular arrest after a trauma are still enormously challenging for both the paramedics and the trauma team in the clinic. This case illustrates the treatment of a 16-year-old boy who suffered a blunt abdominal trauma with a traumatic cardiac arrest followed by an open resuscitation after clamshell thoracotomy. Subsequently, the treatment after damage control is discussed regarding the current literature and recommendations for treatment.

Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies.

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability.

Realizing Distance-Selective Interactions in a Rydberg-Dressed Atom Array.

Measurement-based quantum computing relies on the rapid creation of large-scale entanglement in a register of stable qubits. Atomic arrays are well suited to store quantum information, and entanglement can be created using highly-excited Rydberg states. Typically, isolating pairs during gate operation is difficult because Rydberg interactions feature long tails at large distances. Here, we engineer distance-selective interactions that are strongly peaked in distance through off-resonant laser coupling of molecular potentials between Rydberg atom pairs. Employing quantum gas microscopy, we verify the dressed interactions by observing correlated phase evolution using many-body Ramsey interferometry. We identify atom loss and coupling to continuum modes as a limitation of our present scheme and outline paths to mitigate these effects, paving the way towards the creation of large-scale entanglement.

Every contact leaves a trace: Documenting contamination in lithic residue studies at the Middle Palaeolithic sites of Lusakert Cave 1 (Armenia) and Crvena Stijena (Montenegro).

Investigations of organic lithic micro-residues have, over the last decade, shifted from entirely morphological observations using visible-light microscopy to compositional ones using scanning electron microscopy and Fourier-transform infrared microspectroscopy, providing a seemingly objective chemical basis for residue identifications. Contamination, though, remains a problem that can affect these results. Modern contaminants, accumulated during the post-excavation lives of artifacts, are pervasive, subtle, and even "invisible" (unlisted ingredients in common lab products). Ancient contamination is a second issue. The aim of residue analysis is to recognize residues related to use, but other types of residues can also accumulate on artifacts. Caves are subject to various taphonomic forces and organic inputs, and use-related residues can degrade into secondary compounds. This organic "background noise" must be taken into consideration. Here we show that residue contamination is more pervasive than is often appreciated, as revealed by our studies of Middle Palaeolithic artifacts from two sites: Lusakert Cave 1 in Armenia and Crvena Stijena in Montenegro. First, we explain how artifacts from Lusakert Cave 1, despite being handled following specialized protocols, were tainted by a modern-day contaminant from an unanticipated source: a release agent used inside the zip-top bags that are ubiquitous in the field and lab. Second, we document that, when non-artifact "controls" are studied alongside artifacts from Crvena Stijena, comparisons reveal that organic residues are adhered to both, indicating that they are prevalent throughout the sediments and not necessarily related to use. We provide suggestions for reducing contamination and increasing the reliability of residue studies. Ultimately, we propose that archaeologists working in the field of residue studies must start with the null hypothesis that miniscule organic residues reflect contamination, either ancient or modern, and systematically proceed to rule out all possible contaminants before interpreting them as evidence of an artifact's use in the distant past.

A call for open data to develop mental health digital biomarkers.

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study.

BACKGROUND: Given the interrelated health of children and parents, strategies to promote stress regulation are critically important in the family context. However, the uptake of preventive mental health is limited among parents owing to competing family demands. OBJECTIVE: In this study, we aim to determine whether it is feasible and acceptable to randomize digital prompts designed to engage parents in real-time brief mindfulness activities guided by a commercially available app. METHODS: We conducted a 30-day pilot microrandomized trial among a sample of parents who used Android smartphones. Each day during a parent-specified time frame, participants had a 50% probability of receiving a prompt with a message encouraging them to engage in a mindfulness activity using a commercial app, Headspace. In the 24 hours following randomization, ecological momentary assessments and passively collected smartphone data were used to assess proximal engagement (yes or no) with the app and any mindfulness activity (with or without the app). These data were combined with baseline and exit surveys to determine feasibility and acceptability. RESULTS: Over 4 months, 83 interested parents were screened, 48 were eligible, 16 were enrolled, and 10 were successfully onboarded. Reasons for nonparticipation included technology barriers, privacy concerns, time constraints, or change of mind. In total, 80% (8/10) of parents who onboarded successfully completed all aspects of the intervention. While it is feasible to randomize prompt delivery, only 60% (6/10) of parents reported that the timing of prompts was helpful despite having control over the delivery window. Across the study period, we observed higher self-reported engagement with Headspace on days with prompts (31/62, 50% of days), as opposed to days without prompts (33/103, 32% of days). This pattern was consistent for most participants in this study (7/8, 87%). The time spent using the app on days with prompts (mean 566, SD 378 seconds) was descriptively higher than on days without prompts (mean 225, SD 276 seconds). App usage was highest during the first week and declined over each of the remaining 3 weeks. However, self-reported engagement in mindfulness activities without the app increased over time. Self-reported engagement with any mindfulness activity was similar on days with (40/62, 65% of days) and without (65/103, 63% of days) prompts. Participants found the Headspace app helpful (10/10, 100%) and would recommend the program to others (9/10, 90%). CONCLUSIONS: Preliminary findings suggest that parents are receptive to using mindfulness apps to support stress management, and prompts are likely to increase engagement with the app. However, we identified several implementation challenges in the current trial, specifically a need to optimize prompt timing and frequency as a strategy to engage users in preventive digital mental health.

Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.S. resident physician program. Residents and their supervising attending physicians were presented with medium-fidelity mockups of a dashboard providing behavioral data on residents' sleep, activity and time working; self-reported data on residents' levels of burnout; and a free text box where residents could further contextualize their well-being. Our findings uncover tensions around how best to measure workplace well-being, who within a workplace is accountable for worker stress, and how the introduction of such tools remakes the boundaries of appropriate information flows between worker and workplace. We conclude by charting future work confronting these tensions, to ensure personal sensing is leveraged to truly improve worker well-being.

A revised AMS and tephra chronology for the Late Middle to Early Upper Paleolithic occupations of Ortvale Klde, Republic of Georgia.

The nature and timing of the shift from the Late Middle Paleolithic (LMP) to the Early Upper Paleolithic (EUP) varied geographically, temporally, and substantively across the Near East and Eurasia; however, the result of this process was the archaeological disappearance of Middle Paleolithic technologies across the length and breadth of their geographic distribution. Ortvale Klde rockshelter (Republic of Georgia) contains the most detailed LMP-EUP archaeological sequence in the Caucasus, an environmentally and topographically diverse region situated between southwest Asia and Europe. Tephrochronological investigations at the site reveal volcanic ash (tephra) from various volcanic sources and provide a tephrostratigraphy for the site that will facilitate future correlations in the region. We correlate one of the cryptotephra layers to the large, caldera-forming Nemrut Formation eruption (30,000 years ago) from Nemrut volcano in Turkey. We integrate this tephrochronological constraint with new radiocarbon dates and published ages in an OxCal Bayesian age model to produce a revised chronology for the site. This model increases the ages for the end of the LMP (∼47.5-44.2 ka cal BP) and appearance of the EUP (∼46.7-43.6 ka cal BP) at Ortvale Klde, which are earlier than those currently reported for other sites in the Caucasus but similar to estimates for specific sites in southwest Asia and eastern Europe. These data, coupled with archaeological, stratigraphic, and taphonomic observations, suggest that at Ortvale Klde, (1) the appearance of EUP technologies of bone and stone has no technological roots in the preceding LMP, (2) a LMP population vacuum likely preceded the appearance of these EUP technologies, and (3) the systematic combination of tephra correlations and absolute dating chronologies promises to substantially improve our inter-regional understanding of this critical time interval of human evolution and the potential interconnectedness of hominins at different sites.

Identifying Mobile Sensing Indicators of Stress-Resilience.

Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA). This system identified indicators of resilience during a medical internship, the high stress first-year of a residency program. We then created density estimation approaches to predict these indicators before mental health changes occurred, and validated whether the predicted indicators were also associated with resilience. Our system identified resilience indicators associated with physical activity (step count), sleeping behavior, reduced heart rate, increased mood, and reduced mood variability. Density estimation models were able to replicate a subset of the associations between sleeping behavior, heart rate, and resilience. To the best of our knowledge, this work provides the first methodology to identify and predict indicators of resilience using passive sensing and EMA. Researchers studying resident mental health can apply this approach to design resilience-building interventions and prevent mental health symptom development.

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient's condition worsens. OBJECTIVE: In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse. METHODS: Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied. RESULTS: A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events. CONCLUSIONS: Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.

[Posttraumatic vertebral disc alterations after B and C type spinal injuries in childhood-Clinical and radiological 10-year results for two cases]. / Posttraumatische Bandscheibenalterationen nach B- und C-Verletzungen der Wirbelsäule im Kindesalter ­ Klinische und radiologische Zehnjahresergebnisse für zwei Fälle.

Pediatric traumatic vertebral injuries usually present as stable A (AOspine classification) fractures, whereas B and C injuries are relatively uncommon. In contrast to adults the appropriate treatment strategy in children remains an issue of debate.The data from two pediatric patients admitted with B and C type spinal injuries in 2007 and 2008 were retrospectively analyzed. The initial diagnostics were performed via computed tomography (CT) and an additional magnetic resonance imaging (MRI) was carried out in one case.The clinical and radiological follow-up controls were carried out after 77 and 66 months as well as 123 and 112 months, respectively. In both cases thoracolumbar MRI scans revealed degenerative alterations of the ventral half of the L1/L2 disc with a regular disc signal in the dorsal segment at the first follow-up and a progressive disc degeneration in one patient at the second follow-up.Surgical treatment of pediatric B and C type injuries via open reduction and temporary monosegmental posterior screw and rod instrumentation results in satisfactory clinical and radiological outcomes. In the absence of vertebral burst fractures, the function and stability of discoligamentous injuries in children can be restored without any additional osseous fusion.

Characterization of hippocampal subfields using ex vivo MRI and histology data: Lessons for in vivo segmentation.

Hippocampal subfield segmentation on in vivo MRI is of great interest for cognition, aging, and disease research. Extant subfield segmentation protocols have been based on neuroanatomical references, but these references often give limited information on anatomical variability. Moreover, there is generally a mismatch between the orientation of the histological sections and the often anisotropic coronal sections on in vivo MRI. To address these issues, we provide a detailed description of hippocampal anatomy using a postmortem dataset containing nine specimens of subjects with and without dementia, which underwent a 9.4 T MRI and histological processing. Postmortem MRI matched the typical orientation of in vivo images and segmentations were generated in MRI space, based on the registered annotated histological sections. We focus on the following topics: the order of appearance of subfields, the location of subfields relative to macroanatomical features, the location of subfields in the uncus and tail and the composition of the dark band, a hypointense layer visible in T2-weighted MRI. Our main findings are that: (a) there is a consistent order of appearance of subfields in the hippocampal head, (b) the composition of subfields is not consistent in the anterior uncus, but more consistent in the posterior uncus, (c) the dark band consists only of the CA-stratum lacunosum moleculare, not the strata moleculare of the dentate gyrus, (d) the subiculum/CA1 border is located at the middle of the width of the hippocampus in the body in coronal plane, but moves in a medial direction from anterior to posterior, and (e) the variable location and composition of subfields in the hippocampal tail can be brought back to a body-like appearance when reslicing the MRI scan following the curvature of the tail. Our findings and this publicly available dataset will hopefully improve anatomical accuracy of future hippocampal subfield segmentation protocols.

Geochemical Evidence for the Control of Fire by Middle Palaeolithic Hominins.

The use of fire played an important role in the social and technological development of the genus Homo. Most archaeologists agree that this was a multi-stage process, beginning with the exploitation of natural fires and ending with the ability to create fire from scratch. Some have argued that in the Middle Palaeolithic (MP) hominin fire use was limited by the availability of fire in the landscape. Here, we present a record of the abundance of polycyclic aromatic hydrocarbons (PAHs), organic compounds that are produced during the combustion of organic material, from Lusakert Cave, a MP site in Armenia. We find no correlation between the abundance of light PAHs (3-4 rings), which are a major component of wildfire PAH emissions and are shown to disperse widely during fire events, and heavy PAHs (5-6 rings), which are a major component of particulate emissions of burned wood. Instead, we find heavy PAHs correlate with MP artifact density at the site. Given that hPAH abundance correlates with occupation intensity rather than lPAH abundance, we argue that MP hominins were able to control fire and utilize it regardless of the variability of fires in the environment. Together with other studies on MP fire use, these results suggest that the ability of hominins to manipulate fire independent of exploitation of wildfires was spatially variable in the MP and may have developed multiple times in the genus Homo.

Biomechanical Study of a Novel, Expandable, Non-Metallic and Radiolucent CF/PEEK Vertebral Body Replacement (VBR).

Vertebral body replacement is well-established to stabilize vertebral injuries due to trauma or cancer. Spinal implants are mainly manufactured by metallic alloys; which leads to artifacts in radiological diagnostics; as well as in radiotherapy. The purpose of this study was to evaluate the biomechanical data of a novel carbon fiber reinforced polyetheretherketone (CF/PEEK) vertebral body replacement (VBR). Six thoracolumbar specimens were tested in a six degrees of freedom spine tester. In all tested specimens CF/PEEK pedicle screws were used. Two different rods (CF/PEEK versus titanium) with/without cross connectors and two different VBRs (CF/PEEK prototype versus titanium) were tested. In lateral bending and flexion/extension; range of motion (ROM) was significantly reduced in all instrumented states. In axial rotation; the CF/PEEK combination (rods and VBR) resulted in the highest ROM; whereas titanium rods with titanium VBR resulted in the lowest ROM. Two cross connectors reduced ROM in axial rotation for all instrumentations independently of VBR or rod material. All instrumented states in all planes of motion showed a significantly reduced ROM. No significant differences were detected between the VBR materials in all planes of motion. Less rigid CF/PEEK rods in combination with the CF/PEEK VBR without cross connectors showed the smallest reduction in ROM. Independently of VBR and rod material; two cross connectors significantly reduced ROM in axial rotation. Compared to titanium rods; the use of CF/PEEK rods results in higher ROM. The stiffness of rod material has more influence on the ROM than the stiffness of VBR material.

Characterizing the human hippocampus in aging and Alzheimer's disease using a computational atlas derived from ex vivo MRI and histology.

Although the hippocampus is one of the most studied structures in the human brain, limited quantitative data exist on its 3D organization, anatomical variability, and effects of disease on its subregions. Histological studies provide restricted reference information due to their 2D nature. In this paper, high-resolution (∼200 × 200 × 200 µm3) ex vivo MRI scans of 31 human hippocampal specimens are combined using a groupwise diffeomorphic registration approach into a 3D probabilistic atlas that captures average anatomy and anatomic variability of hippocampal subfields. Serial histological imaging in 9 of the 31 specimens was used to label hippocampal subfields in the atlas based on cytoarchitecture. Specimens were obtained from autopsies in patients with a clinical diagnosis of Alzheimer's disease (AD; 9 subjects, 13 hemispheres), of other dementia (nine subjects, nine hemispheres), and in subjects without dementia (seven subjects, nine hemispheres), and morphometric analysis was performed in atlas space to measure effects of age and AD on hippocampal subfields. Disproportional involvement of the cornu ammonis (CA) 1 subfield and stratum radiatum lacunosum moleculare was found in AD, with lesser involvement of the dentate gyrus and CA2/3 subfields. An association with age was found for the dentate gyrus and, to a lesser extent, for CA1. Three-dimensional patterns of variability and disease and aging effects discovered via the ex vivo hippocampus atlas provide information highly relevant to the active field of in vivo hippocampal subfield imaging.

Middle Palaeolithic toolstone procurement behaviors at Lusakert Cave 1, Hrazdan valley, Armenia.

Strategies employed by Middle Palaeolithic hominins to acquire lithic raw materials often play key roles in assessing their movements through the landscape, relationships with neighboring groups, and cognitive abilities. It has been argued that a dependence on local resources is a widespread characteristic of the Middle Palaeolithic, but how such behaviors were manifested on the landscape remains unclear. Does an abundance of local toolstone reflect frequent encounters with different outcrops while foraging, or was a particular outcrop favored and preferentially quarried? This study examines such behaviors at a finer geospatial scale than is usually possible, allowing us to investigate hominin movements through the landscape surrounding Lusakert Cave 1 in Armenia. Using our newly developed approach to obsidian magnetic characterization, we test a series of hypotheses regarding the locations where hominins procured toolstone from a volcanic complex adjacent to the site. Our goal is to establish whether the cave's occupants procured local obsidian from preferred outcrops or quarries, secondary deposits of obsidian nodules along a river, or a variety of exposures as encountered while moving through the river valley or across the wider volcanic landscape during the course of foraging activities. As we demonstrate here, it is not the case that one particular outcrop or deposit attracted the cave occupants during the studied time intervals. Nor did they acquire obsidian at random across the landscape. Instead, our analyses support the hypothesis that these hominins collected obsidian from outcrops and exposures throughout the adjacent river valley, reflecting the spatial scale of their day-to-day foraging activities. The coincidence of such behaviors within the resource-rich river valley suggests efficient exploitation of a diverse biome during a time interval immediately preceding the Middle to Upper Palaeolithic "transition," the nature and timing of which has yet to be determined for the region.

A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe.

Automatic segmentation of cortical and subcortical structures is commonplace in brain MRI literature and is frequently used as the first step towards quantitative analysis of structural and functional neuroimaging. Most approaches to brain structure segmentation are based on propagation of anatomical information from example MRI datasets, called atlases or templates, that are manually labeled by experts. The accuracy of automatic segmentation is usually validated against the "bronze" standard of manual segmentation of test MRI datasets. However, good performance vis-a-vis manual segmentation does not imply accuracy relative to the underlying true anatomical boundaries. In the context of segmentation of hippocampal subfields and functionally related medial temporal lobe cortical subregions, we explore the challenges associated with validating existing automatic segmentation techniques against underlying histologically-derived anatomical "gold" standard; and, further, developing automatic in vivo MRI segmentation techniques informed by histological imaging.