Cocaine use disorder (CUD) is a substance use disorder characterized by a strong desire to obtain, consume, and misuse cocaine. Little is known about how cocaine affects the structure of the brain. ...In this study, we first investigated the anatomical brain changes in individuals with CUD compared to their matched healthy controls, and then explored whether these anatomical brain abnormalities contribute to considerably accelerated brain aging among this population.
: At the first stage, we used anatomical magnetic resonance imaging (MRI) data, voxel-based morphometry (VBM), and deformation-based morphometry techniques to uncover the morphological and macroscopic anatomical brain changes in 74 CUD patients compared to 62 age- and sex-matched healthy controls (HCs) obtained from the SUDMEX CONN dataset, the Mexican MRI dataset of patients with CUD. Then, we computed brain-predicted age difference (i.e., brain-PAD: the brain-predicted age minus the actual age) in CUD and HC groups using a robust brain age estimation framework. Using a multiple regression analysis, we also investigated the regional gray matter (GM) and white matter (WM) changes associated with the brain-PAD.
: Using a whole-brain VBM analysis, we observed widespread gray matter atrophy in CUD patients located in the temporal lobe, frontal lobe, insula, middle frontal gyrus, superior frontal gyrus, rectal gyrus, and limbic lobe regions compared to the HCs. In contrast, we did not observe any swelling in the GM, changes in the WM, or local brain tissue atrophy or expansion between the CUD and HC groups. Furthermore, we found a significantly higher brain-PAD in CUD patients compared to matched HCs (mean difference = 2.62 years, Cohen's d = 0.54;
-test = 3.16,
= 0.002). The regression analysis showed significant negative changes in GM volume associated with brain-PAD in the CUD group, particularly in the limbic lobe, subcallosal gyrus, cingulate gyrus, and anterior cingulate regions.
: The results of our investigation reveal that chronic cocaine use is linked to significant changes in gray matter, which hasten the process of structural brain aging in individuals who use the drug. These findings offer valuable insights into the impact of cocaine on the composition of the brain.
Abstract We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can ...predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting.
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for ...epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
Sex differences play a vital role in human brain structure and physiology. Previous reports have proposed evidence hinting at a metabolic advantage in female brains across adulthood. It remained to ...be determined whether this advantage would be maintained across the spectrum of cognitive impairment, up to and including dementia due to Alzheimer's disease (AD). Here, using a machine-learning algorithm, we explore sex differences in metabolic brain-age derived from fluorodeoxyglucose positron emission tomography imaging among cognitively healthy individuals and those affected by mild cognitive impairment and clinically probable AD. First, we report that cognitively healthy male participants showed a persistently “older” looking brains when compared to healthy female participants in term of metabolic brain age, confirming earlier reports. However, this distinction disappeared among MCI individuals and probable AD patients, and this loss could not be explained by an accompanying neurodegeneration. This would seem to indicate that females have a higher rate of decline in brain glucose metabolism when cognitively impaired to negate their prior advantage.
•We present a robust and simple bias-adjustment scheme for neuroimaging-based brain age frameworks.•The efficiency of proposed bias-adjustment scheme was assessed in the context of cognitively ...healthy aging and Alzheimer's disease.•The proposed bias-adjustment scheme was shown efficient and statistically improved results, making it a necessary part for future brain age frameworks.
The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R2 of 0.81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2.66 years on 75 cognitively unimpaired adults as an independent set; whereas an R2 of 0.24 and a mean absolute error of 4.71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings.
Parkinson's disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current ...stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.
Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual's ..."brain-age" from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with inter-ictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epilepsy.
Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early ...anatomical brain changes that precede the transition from healthy cognition to MCI in PD.
Structural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson's Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC,
= 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC,
= 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier.
The average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain.
Our machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.
Energy balance in Iran's agronomy (1990–2006) Beheshti Tabar, Iman; Keyhani, Alireza; Rafiee, Shaheen
Renewable & sustainable energy reviews,
02/2010, Letnik:
14, Številka:
2
Journal Article
Recenzirano
In this study data from 17 years (1990–2006) were collected to determine energy intensive areas and evaluate energy parameters of Iran's agronomy sector. All the direct and indirect inputs of energy ...for the production of 19 agricultural commodities were evaluated. The inputs and outputs were calculated by multiplying the amounts of inputs and outputs by their energy equivalents. The results indicated that total energy input increased from 32.40
GJ
ha
−1 in 1990 to 37.20
GJ
ha
−1 in 2006. At the same period, total output energy increased from 30.85 to 43.68
GJ
ha
−1. The results show that irrigation with 40.0% and fertilizer (28.4%) had the highest share in energy consumption. The average net energy gain was a positive value; however, about 87% of the input energy emanates from non-renewable sources of energy. The mean energy ratio was estimated to be 1.07 and showed an increasing trend during the period rising from 0.95 in 1990 to 1.17 in 2006. This indicates that increased use of inputs ha
−1 in production was accompanied by a larger increase in the output levels. It can be inferred from the results that improvements in irrigation and fertilizer application can significantly affect the energy efficiency of Iranian agriculture.
The identification of the brain morphological alterations that play important roles in neurodegenerative/neurological diseases will contribute to our understanding of the causes of these diseases. ...Various automated software programs are designed to provide an automatic framework to detect brain morphological changes in structural magnetic resonance imaging (MRI) data. A voxel-based morphometry (VBM) analysis can also be used for the detection of brain volumetric abnormalities. Here, we compared gray matter (GM) and white matter (WM) abnormality results obtained by a VBM analysis using the Computational Anatomy Toolbox (CAT12) via the current version of Statistical Parametric Mapping software (SPM12) with the results obtained by a VBM analysis using the VBM8 toolbox implemented in the older software SPM8, in adult temporal lobe epilepsy (TLE) patients with (
= 51) and without (
= 57) hippocampus sclerosis (HS), compared to healthy adult controls (
= 28). The VBM analysis using CAT12 showed that compared to the healthy controls, significant GM and WM reductions were located in ipsilateral mesial temporal lobes in the TLE-HS patients, and slight GM amygdala swelling was present in the right TLE-no patients (
= 27). In contrast, the VBM analysis via the VBM8 toolbox showed significant GM and WM reductions only in the left TLE-HS patients (
= 25) compared to the healthy controls. Our findings thus demonstrate that compared to VBM8, a VBM analysis using CAT12 provides a more accurate volumetric analysis of the brain regions in TLE. Our results further indicate that a VBM analysis using CAT12 is more robust and accurate against volumetric alterations than the VBM8 toolbox.
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