Neuroimaging Research Articles

Prognostic enrichment for early-stage Huntington’s disease: An explainable machine learning approach for clinical trial

BackgroundIn Huntington’s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process. ObjectivesTo improve stratification of Huntington’s disease individuals for clinical trials. MethodsWe used data from 451 gene positive individuals with Huntington’s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement. ResultsThe predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %). ConclusionsThis study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation.

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy.

Magnetic resonance imaging techniques for indirect assessment of myelin content in the brain using standard T1w and T2w MRI sequences and postprocessing analysis.

Magnetic Resonance Imaging (MRI) has revolutionized our ability to non-invasively study the brain's structural and functional properties. However, detecting myelin, a crucial component of white matter, remains challenging due to its indirect visibility on conventional MRI scans. Myelin plays a vital role in neural signal transmission and is associated with various neurological conditions. Understanding myelin distribution and content is crucial for insights into brain development, aging, and neurological disorders. Although specialized MRI sequences can estimate myelin content, these are time-consuming. Also, many patients sent to specialized neurological centers have an MRI of the brain already scanned. In this study, we focused on techniques utilizing standard MRI T1-weighted (T1w) and T2 weighted (T2w) sequences commonly used in brain imaging protocols. We evaluated the applicability of the T1w/T2w ratio in assessing myelin content by comparing it to quantitative T1 mapping (qT1). Our study included 1 healthy adult control and 7 neurologic patients (comprising both pediatric and adult populations) with epilepsy originating from focal epileptogenic lesions visible on MRI structural scans. Following image acquisition on a 3T Siemens Vida scanner, datasets were co registered, and segmented into anatomical regions using the Fastsurfer toolbox, and T1w/T2w ratio maps were calculated in Matlab software. We further assessed interhemispheric differences in volumes of individual structures, their signal intensity, and the correlation of the T1w/T2w ratio to qT1. Our data demonstrate that in situations where a dedicated myelin-sensing sequence such as qT1 is not available, the T1w/T2w ratio provides significantly better information than T1w alone. By providing indirect information about myelin content, this technique offers a valuable tool for understanding the neurobiology of myelin-related conditions using basic brain scans.

Spatial complexity of the cerebral cortex pial surface: quantitative assessment by two-dimensional fractal analysis of MRI brain scans

Spatial complexity of the cerebral cortex pial surface: quantitative assessment by two-dimensional fractal analysis of MRI brain scans

McTFI QSM MRI ABC/2 intracranial hemorrhage to noncontrast head CT volume measurement equivalence

Background/objectivesIntracranial hemorrhage (ICH) volume assessment is an important part of patient management and is routinely obtained by non-contrast head CT (NCHCT) using the validated ABC/2 measurement method. Because conventional MRI imaging sequences demonstrate variability in ICH appearance, volumetric analyses for MRI bleed volume in a standardized manner using ABC/2 is not possible. The recently introduced multiecho-complex total field inversion quantitative susceptibility mapping (mcTFI QSM) MRI technique, which maps brain tissue susceptibility to both depict brain tissue structures and quantify tissue susceptibility, may provide a viable alternative. In this study we evaluated mcTFI QSM ABC/2 ICH volume assessment relative to NCHCT. MethodsPatients with ICH who had undergone NCHCT and MRI brain scans within 48 h were recruited for this retrospective study. The ABC/2 method was applied to estimate the bleed volume for both NCHCT and MRI by a CAQ-certified neuroradiologist with 10 years of experience and a trained laboratory assistant. Results were analyzed via Bland-Altman (B-A) and linear regression. Results54 patients (27 females) who had undergone NCHCT and MRI within 48 h (<24 h., n = 31, 24-48 h, n = 10) were enrolled. mcTFI QSM ICH volume measurement method showed a positive correlation (99.5%) compared to NCHCT. B-A plot comparing ABC/2 ICH volume on NCHCT and mcTFI MRI done for patients within 24 h demonstrates a bias of −0.09%. ConclusionsICH volume calculation using ABC/2 on mcTFI QSM showed a high correlation with NCHCT measurement. These results suggest mcTFI QSM is a promising MRI method for ABC/2 for bleed volume measurement.

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Background: Gliomas represent the most prevalent type of primary brain tumor and are associated with a significant mortality rate. The utilization of MR cerebral perfusion proves effective in distinguishing between low-grade and high-grade gliomas, surpassing the capabilities of routine MRI. This distinction allows for the timely initiation of surgical intervention. We aimed to document the diagnostic accuracy of MR cerebral perfusion against the gold standard of lab-based histopathology. Methods: We conducted this cross-sectional validation study over a duration of six months in the outpatient radiology department of a major public sector hospital of Rawalpindi, Pakistan. All patients had MR cerebral perfusion Scan of brain in order to differentiate low grade and high grade glioma. Quantitative rCBV analysis was carried out adopting a threshold value of 3, above this value the lesion was considered at a high grade. The results were written on specially designed proforma. Results: A total 230 patients were included in the study with mean age of 50.68+16.36 and there were 135 (58.7%) male and 95 (41.3%) were female. There were 122 (53.0%) patients presented with low grade glioma and 108 (47.0%) patients presented with high grade glioma. The sensitivity, specificity, PPV, NPV and diagnostic accuracy of MR cerebral perfusion to differentiate between low grade and high grade gliomas was 85.71%, 70.97%, 88.89%, 64.71% and 81.74% respectively. Conclusion: This study concludes that the MR perfusion reliably identifies higher-grade features in a tumor that otherwise may not appear on standard MRI scan.

Design of pseudo brain nano gold layer photonic crystal fiber sensor and investigation of fabrication accuracy

In this research, a new photonic crystal fiber sensor is proposed based on human brain scan images. As the human brain analyzes information in the cerebellum to make decisions, the use of this design and placement of air holes in the shape of human brain is the main reason of more electromagnetic field limitation in the center of the fiber or pseudo-cerebellum. Finite element method based on mode solver (FEM) is used to analyze this sensor. The size of the air holes and the thickness of the plasmonic layer are proposed in this design based on the Nelder-Mead optimization algorithm. The possible errors caused by the sensor fabrication are investigated and a unique equation is presented to estimate the effect of the size of the air holes on the sensor confinement loss. Numerical inference show that this brain shape sensor has an amazing maximum amplitude sensitivity (AS) of 7208 RIU−1 and appropriate maximum wavelength sensitivity (WS) of 10000 nm/RIU. Therefore, the proposed sensor has great prospects for medical, chemical and pharmaceutical measurements and diagnostic applications because of its high sensitivity, powerful detection and easy fabrication.

Effects of healthy aging and mnemonic strategies on verbal memory performance across the adult lifespan: Mediating role of posterior hippocampus.

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.

BRAIN TUMOR DETECTION

Brain tumor detection is a significant problem in medical diagnostics since early and accurate detection improves patient outcomes. Traditional tumor identification techniques often depend on manual interpretation of medical examination, which can be time-consuming and prone to humanerror. Algorithms based on deep learning have emerged in recent years as a viable way to automateand enhance brain tumor identification using medical imaging data. This paper conveys an extensive look on the use of deep learning for brain tumor identification. A Convolutional NeuralNetwork(CNN) architecture is put forward to reach minimum accuracy of 97% and maximum of 100%, using its abilities to automatically learn hierarchical attributes from medical imagery that involve Magnetic Resonance Imaging(MRI) scans. To learn discriminative features suggestive oftumor presence, the suggested CNN framework is trained an extensive collection of labeled brainMRI images. The findings from experiments show that the proposed deep learning approach works. The trained CNN is quite good at differentiating between tumor and non-tumor regions in brain scans. Furthermore, cross-validation and unbiased evaluation are used to assess the model’scapacity to generalize to data that was previously unavailable. Deep learning in brain tumor identification has the potential to greatly enhance diagnostic accuracy, reduce human error, and speed up decision-making. As deep learning research advances, future studies may look at the amalgamation of multi-modal imaging data, transfer learning, and ensemble techniques in order to boost the robustness and generalizability of brain tumor diagnosis. The proposed deep learning-based brain tumor detection system offers the potential for improving medical professionals’ capacity to properly and instantly diagnose brain tumors, ultimately leading to improvements in patient care and outcomes. Keywords: Brain Tumor detection, Diagnosis, Deep Learning, Convolutional NeuralNetworks, Pooling, MRI Dataset

Study of Machine Learning Techniques in Autism Spectrum Disorder Detection

ASD is a neurodevelopmental disorder that affects how people communicate, interact, and behave. It is not a single condition, but a spectrum of different symptoms and challenges that can vary from person to person. To diagnose ASD, clinicians and experts use various tests and observations, but these methods can have some drawbacks. They can be subjective, expensive, and time-consuming, and they may not work well for different cultures and regions. There is also no clear biological or genetic marker for ASD. ML is a type of artificial intelligence that can learn from data and make predictions or decisions without being programmed. ML can help with ASD diagnosis in different ways, such as screening, feature extraction, classification, and outcome prediction. Screening is the process of finding out who may have ASD and need more tests. Feature extraction is the process of getting useful information from the data, such as brain scans, speech patterns, or eye movements. Classification is the process of putting people into different groups or categories, such as ASD or non-ASD. Outcome prediction is the process of estimating the future situation or performance of people, such as their thinking skills or well-being. In this paper, we review the latest ML methods for ASD diagnosis and the challenges and limitations of these methods. We also talk about the ethical and social issues of using ML for ASD diagnosis and suggest some ideas for future research.

Acute subdural hematoma in a footballer following a head-ball of the ball: a rare neurosurgical complication

Acute subdural hematoma is a frequent complication of high-energy head injuries. Herein, we report an unusual case of this condition that occurred in a young footballer after he head-balled a football. A 37-year-old footballer, during a match, jumped and gave a head shot on the ball. Upon landing, he felt severe headaches forcing him out of the stadium. About 4 h later, he lost consciousness and went into a coma. After resuscitation, he remained clouded, with cephalalgia and had left hemiparesis. The performed brain scan revealed an acute right fronto-parietal subdural hematoma and the patient benefited from craniotomy surgery in our department with simple postoperative suites. The authors, after a brief discussion of the biomechanics of this serious condition, ask all the actors of this very popular sport, to consider the possibility of occurrence of this condition in players.

Environmental exposure to metallic pollution impairs honey bee brain development and cognition

Laboratory studies show detrimental effects of metallic pollutants on invertebrate behaviour and cognition, even at low levels. Here we report a field study on Western honey bees exposed to metal and metalloid pollution through dusts, food and water at a historic mining site. We analysed more than 1000 bees from five apiaries along a gradient of contamination within 11 km of a former gold mine in Southern France. Bees collected close to the mine exhibited olfactory learning performances lower by 36% and heads smaller by 4%. Three-dimensional scans of bee brains showed that the olfactory centres of insects sampled close to the mine were also 4% smaller, indicating neurodevelopmental issues. Our study raises serious concerns about the health of honey bee populations in areas polluted with potentially harmful elements, particularly with arsenic, and illustrates how standard cognitive tests can be used for risk assessment.

Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging

Abstract Given that scientific practices contribute to the climate crisis, scientists should reflect on the planetary impact of their work. Research computing can have a substantial carbon footprint in cases where researchers employ computationally expensive processes with large amounts of data. Analysis of human neuroimaging data, such as Magnetic Resonance Imaging brain scans, is one such case. Here, we consider ten ways in which those who conduct human neuroimaging research can reduce the carbon footprint of their research computing, by making adjustments to the ways in which studies are planned, executed, and analysed; as well as where and how data are stored.

Safety and efficacy of factor XIa inhibition with milvexian for secondary stroke prevention (AXIOMATIC-SSP): a phase 2, international, randomised, double-blind, placebo-controlled, dose-finding trial

People with factor XI deficiency have lower rates of ischaemic stroke than the general population and infrequent spontaneous bleeding, suggesting that factor XI has a more important role in thrombosis than in haemostasis. Milvexian, an oral small-molecule inhibitor of activated factor XI, added to standard antiplatelet therapy, might reduce the risk of non-cardioembolic ischaemic stroke without increasing the risk of bleeding. We aimed to estimate the dose-response of milvexian for recurrent ischaemic cerebral events and major bleeding in patients with recent ischaemic stroke or transient ischaemic attack (TIA). AXIOMATIC-SSP was a phase 2, randomised, double-blind, placebo-controlled, dose-finding trial done at 367 hospitals in 27 countries. Eligible participants aged 40 years or older, with acute (<48 h) ischaemic stroke or high-risk TIA, were randomly assigned by a web-based interactive response system in a 1:1:1:1:1:2 ratio to receive one of five doses of milvexian (25 mg once daily, 25 mg twice daily, 50 mg twice daily, 100 mg twice daily, or 200 mg twice daily) or matching placebo twice daily for 90 days. All participants received clopidogrel 75 mg daily for the first 21 days and aspirin 100 mg daily for the first 90 days. Investigators, site staff, and participants were masked to treatment assignment. The primary efficacy endpoint was the composite of ischaemic stroke or incident covert brain infarct on MRI at 90 days, assessed in all participants allocated to treatment who completed a follow-up MRI brain scan, and the primary analysis assessed the dose-response relationship with Multiple Comparison Procedure-Modelling (MCP-MOD). The main safety outcome was major bleeding at 90 days, assessed in all participants who received at least one dose of the study drug. This trial is registered with ClinicalTrials.gov (NCT03766581) and the EU Clinical Trials Register (2017-005029-19). Between Jan 27, 2019, and Dec 24, 2021, 2366 participants were randomly allocated to placebo (n=691); milvexian 25 mg once daily (n=328); or twice-daily doses of milvexian 25 mg (n=318), 50 mg (n=328), 100 mg (n=310), or 200 mg (n=351). The median age of participants was 71 (IQR 62-77) years and 859 (36%) were female. At 90 days, the estimates of the percentage of participants with either symptomatic ischaemic stroke or covert brain infarcts were 16·8 (90·2% CI 14·5-19·1) for placebo, 16·7 (14·8-18·6) for 25 mg milvexian once daily, 16·6 (14·8-18·3) for 25 mg twice daily, 15·6 (13·9-17·5) for 50 mg twice daily, 15·4 (13·4-17·6) for 100 mg twice daily, and 15·3 (12·8-19·7) for 200 mg twice daily. No significant dose-response was observed among the five milvexian doses for the primary composite efficacy outcome. Model-based estimates of the relative risk with milvexian compared with placebo were 0·99 (90·2% CI 0·91-1·05) for 25 mg once daily, 0·99 (0·87-1·11) for 25 mg twice daily, 0·93 (0·78-1·11) for 50 mg twice daily, 0·92 (0·75-1·13) for 100 mg twice daily, and 0·91 (0·72-1·26) for 200 mg twice daily. No apparent dose-response was observed for major bleeding (four [1%] of 682 participants with placebo, two [1%] of 325 with milvexian 25 mg once daily, two [1%] of 313 with 25 mg twice daily, five [2%] of 325 with 50 mg twice daily, five [2%] of 306 with 100 mg twice daily, and five [1%] of 344 with 200 mg twice daily). Five treatment-emergent deaths occurred, four of which were considered unrelated to the study drug by the investigator. Factor XIa inhibition with milvexian, added to dual antiplatelet therapy, did not substantially reduce the composite outcome of symptomatic ischaemic stroke or covert brain infarction and did not meaningfully increase the risk of major bleeding. Findings from our study have informed the design of a phase 3 trial of milvexian for the prevention of ischaemic stroke in patients with acute ischaemic stroke or TIA. Bristol Myers Squibb and Janssen Research & Development.

Multi-omics analyses of gut microbiota via 16S rRNA gene sequencing, LC-MS/MS and diffusion tension imaging reveal aberrant microbiota-gut-brain axis in very low or extremely low birth weight infants with white matter injury

ObjectiveThe goal of this study was to comprehensively investigate the characteristics of gut microbiota dysbiosis and metabolites levels in very low or extremely low birth weight (VLBW/ELBW) infants with white matter injury (WMI).MethodsIn this prospective cohort study, preterm infants with gestational age < 32 weeks and weight < 1.5 kg were investigated. Additionally, fecal samples were collected on days zero, 14d and 28d after admission to the intensive care unit. All subjects underwent brain scan via MRI and DTI at a corrected gestational age of 37 ~ 40 weeks. Based on the results of MRI examination, the VLBW/ELBW infants were divided into two groups: WMI and non-WMI. Finally, based on a multi-omics approach, we performed 16S rRNA gene sequencing, LC-MS/MS, and diffusion tension imaging to identify quantifiable and informative biomarkers for WMI.ResultWe enrolled 23 patients with and 48 patients without WMI. The results of 16S RNA sequencing revealed an increase in the number of Staphylococcus and Acinetobacter species in the fecal samples of infants with WMI, as well as increasing levels of S. caprae and A._johnsonii. LEfSe analysis (LDA ≥ 4) showed that the WMI group carried an abundance of Staphylococcus species including S. caprae, members of the phyla Bacteroidota and Actinobacteriota, and Acinetobacter species. A total of 139 metabolic markers were significantly and differentially expressed between WMI and nWMI. KEGG pathway enrichment analysis revealed that the WMI group showed significant downregulation of 17 metabolic pathways including biosynthesis of arginine and primary bile acids. The WMI group showed delayed brain myelination, especially in the paraventricular white matter and splenium of corpus callosum. Staphylococcus species may affect WMI by downregulating metabolites such as cholic acid, allocholic acid, and 1,3-butadiene. Gut microbiota such as Acinetobacter and Bacteroidetes may alter white matter structurally by upregulating metabolites such as cinobufagin.ConclusionBased on 16S RNA sequencing results, severe gut microbiota dysbiosis was observed in the WMI group. The results might reveal damage to potential signaling pathways of microbiota-gut-brain axis in gut microbiota. The mechanism was mediated via downregulation of the bile acid biosynthetic pathway.

Individual differences in rhythm perception modulate music-related motor learning: a neurobehavioral training study with children

Rhythm and motor function are intrinsically linked to each other and to music, but the rhythm-motor interplay during music training, and the corresponding brain mechanisms, are underexplored. In a longitudinal training study with children, we examined the role of rhythm predisposition in the fine motor improvements arising from music training, and which brain regions would be implicated. Fifty-seven 8-year-olds were assigned to either a 6-month music training (n = 21), sports training (n = 18), or a control group (n = 18). They performed rhythm and motor tasks, and structural brain scans before and after training were collected. Better ability to perceive rhythm before training was related to less gray matter volume in regions of the cerebellum, fusiform gyrus, supramarginal gyrus, ventral diencephalon, amygdala, and inferior/middle temporal gyri. Music training improved motor performance, and greater improvements correlated with better pre-training rhythm discrimination. Music training also induced a loss of gray matter volume in the left cerebellum and fusiform gyrus, and volume loss correlated with higher motor gains. No such effects were found in the sports and control groups. In summary, children with finer-tuned rhythm perception abilities were prone to finer motor improvements through music training, and this rhythm-motor link was to some extent subserved by the left cerebellum and fusiform gyrus. These findings have implications for models on music-related plasticity and rhythm cognition, and for programs targeting motor function.

Correlation between smoking and brain atrophy detected by cross sectional imaging

Background and objectives: Smoking have effect on many organs in human body, we are focusing on its relation with brain atrophy. We aimed to determine whether there is an association between smoking and brain atrophy by using certain parameters from CT scan or MRI of brain. Methods: This is a case control study done from December 2019 to May 2021, a total of 100 cases of CT scans and MRI brain which is randomly selected reviewed by taking certain measurements like frontal horn index, Evans ratio, bicaudate index, huckman number, cella media. Results: Brain atrophy group involved 50 cases and control non-atrophy group 50 cases both group with equal number of male and female, there is statistically significant difference between smokers with brain atrophy 30% and smokers with no brain atrophy 6% with p-value 0.027, also the mean duration of smoking among brain atrophy was 17.83 years while among non-atrophic brain was 9.83 years. Number of cigarettes smoked per day showed statistically significant difference as it was 21.07 cigarettes among smokers with brain atrophy versus 13.83 cigarettes in non –brain atrophy smokers. Conclusions: Current brain imaging demonstrate that chronic tobacco smoking is associated with significant increase in prevalence of brain atrophy than non-smokers, smoking can be considered a risk factor for brain atrophy.

SPATIAL AND STRUCTURAL COMPLEXITY OF CEREBRAL HEMISPHERES IN MALE AND FEMALE BRAIN: FRACTAL AND QUANTITATIVE ANALYSES OF MRI BRAIN SCANS

Objectives: The aim of the present study was to compare the features of the structural complexity of the cerebral hemispheres in men and women using fractal analysis of outlined and skeletonized images, as well as quantitative analysis of digital skeletons of the cerebral hemispheres. Material and Methods: Magnetic resonance imaging brain scans of 100 individuals aged 18-86 years (44 males and 56 females) were investigated. Five tomographic sections of each brain were selected for morphometric study (4 coronal and 1 axial sections). The sections were preprocessed, and outlined and skeletonized images were obtained. Fractal analysis was conducted using the two-dimensional box counting method, and fractal dimensions of outlined and skeletonized images were determined. Additionally, quantitative analysis of skeletonized images was performed, determining the following parameters: branches, junctions, end-point voxels, junction voxels, slab voxels, triple points, quadruple points, average branch length, and maximum branch length. Results: We observed that both variants of fractal dimension in males and females did not show significant differences, although most quantitative parameters in males were larger than those in females. Conclusions: The spatial and structural complexity of the cerebral hemispheres, as characterized by fractal dimensions, is almost indistinguishable between males and females. However, in some individual tomographic sections, the male brain may exhibit a slightly higher number of end-point voxels, corresponding to the gyri of the cerebral hemispheres. The obtained data can be used in clinical practice for diagnostic purposes (e.g., for detecting malformations) and for theoretical studies in neuroanatomy.

I know why the caged bird sings: Distress tolerant individuals show greater resting state connectivity between ventromedial prefrontal cortex and right amygdala as a function of higher vagal tone

BackgroundIntolerance to psychological distress is associated with various forms of psychopathology, ranging from addiction to mood disturbance. The capacity to withstand aversive affective states is often explained by individual differences in cardiovagal tone as well as resting state connectivity of the ventromedial prefrontal cortex (vmPFC), a region involved in the regulation of emotions and cardio-autonomic tone. However, it is unclear which brain regions involved in distress tolerance show greater resting state functional connectivity (rsFC) as a function of resting heart rate variability (HRV). MethodsOne-hundred and twenty-six adults, aged 20 to 83.5 years, were selected from a lifespan cohort at the Nathan Kline Institute-Rockland Sample. Participants' distress tolerance levels were assessed based upon performance on the Behavioral Indicator of Resiliency to Distress (BIRD) task. Artifact-free resting-state functional brain scans collected during separate sessions were used. While inside the scanner, a pulse oximeter was used to record beat-to-beat intervals to derive high-frequency heart rate variability (HF-HRV). The relationship between HF-HRV and vmPFC to whole brain functional connectivity was compared between distress tolerant (BIRD completers) and distress intolerant (BIRD non-completers). ResultsGroups did not differ in their history of psychiatric diagnosis. Higher resting HF-HRV was associated with longer total time spent on the BIRD task for the entire sample (r = 0.255, p = 0.004). After controlling for age, gender, body mass index, head motion, and gray matter volume. Distress tolerant individuals showed greater rsFC (p < 0.005 (uncorrected), k = 20) between the vmPFC and default-mode network (DMN) hubs including posterior cingulate cortex/precuneus, medial temporal lobes, and the parahippocampal cortex. As a function of higher resting HF-HRV greater vmPFC connectivity was observed with sub-threshold regions in the right amygdala and left anterior prefrontal cortex, with the former passing small volume correction, in distress tolerant versus distress intolerant individuals. ConclusionIn a lifespan sample of community-dwelling adults, distress tolerant individuals showed greater vmPFC connectivity with anterior and posterior hubs of the DMN compared to distress intolerant individuals. As a function of greater HF-HRV, distress tolerant individuals evidenced greater vmPFC with salience and executive control network hubs. These findings are consistent with deficits in neural resource allocation within a triple network resting amongst persons exhibiting behavioral intolerance to psychological distress.

Neuroimaging Correlates of Alzheimer’s Disease biomarkers in a mid‐life, racially diverse high‐risk cohort

AbstractBackgroundClinicians and researchers utilize neuroimaging (NI) biomarkers of Alzheimer’s disease (AD) at an increasing rate. It is crucial that we determine whether these biomarkers generalize to underrepresented populations, particularly Black Americans (BAs), as they are 64% more likely as white individuals to develop AD. BAs may exhibit unique AD biomarker profiles across disease states, including NI biomarkers. Investigating biomarkers in at‐risk individuals may allow for early identification and intervention. Here we analyze comprehensive NI biomarkers including resting state fMRI, white matter lesions, and hippocampal volumes in conjunction with cerebrospinal fluid (CSF) markers in a midlife, racially diverse, cognitively normal cohort with a parental history of AD.MethodData included 57 cognitively normal, middle‐aged individuals, 14 BAs and 43 white. CSF was acquired via lumbar puncture to determine CSF t‐tau and Aβ1‐42 concentrations. Using resting state fMRI data, we analyzed default mode network connectivity between the posterior cingulate &amp; precuneus, parrahippocampal gyrus (PHG), temporal pole, hippocampus, and lateral temporal cortex. We obtained regional WMH data from FLAIR images using an in‐house algorithm, and hippocampal volumes from Freesurfer. In our multivariate model, outcome variables were connectivity values, regional WMH and hippocampal volumes, with race and CSF t‐tau and Aβ1‐42 as our independent variables with a race X CSF interaction term.ResultWe identified a significant race X t‐tau and race X Aβ1‐42 interaction term for temporal and parietal WMH volumes and connectivity between PHG and temporal pole. Higher AD CSF biomarkers negatively correlated with brain connectivity (p = 0.01), and positively with WMH volume (p&lt;0.001) in BAs.ConclusionWe extend our previous work to a middle‐aged cohort to show that DMN connectivity may be a predictor of AD risk in middle‐age, particularly for BAs, and that BAs may exhibit earlier vulnerability to vascular lesions. We support previous work that the temporal lobe is the first region to experience AD‐related connectivity changes, and that WMHs may predispose this region to AD vulnerability. This study emphasizes the need for recruitment of diverse cohorts in NI studies of AD.