Risk factors for brain health in agricultural work: a systematic review

Includes bibliographical references.Certain exposures related to agricultural work have been associated with neurological disorders. To date, few studies have included brain health measurements to link specific risk factors with possible neural mechanisms. Moreover, a synthesis of agricultural risk factors associated with poorer brain health outcomes is missing. In this systematic review, we identified 106 articles using keywords related to agriculture, occupational exposure, and the brain. We identified seven major risk factors: non-specific factors that are associated with agricultural work itself, toluene, pesticides, heavy metal or dust exposure, work with farm animals, and nicotine exposure from plants. Of these, pesticides are the most highly studied. The majority of qualifying studies were epidemiological studies. Nigral striatal regions were the most well studied brain area impacted. Of the three human neuroimaging studies we found, two focused on functional networks and the third focused on gray matter. We identified two major directions for future studies that will help inform preventative strategies for brain health in vulnerable agricultural workers: (1) the effects of moderators such as type of work, sex, migrant status, race, and age; and (2) more comprehensive brain imaging studies, both observational and experimental, involving several imaging techniques

White matter plasticity in healthy older adults: The effects of aerobic exercise

White matter deterioration is associated with cognitive impairment in healthy aging and Alzheimer\u27s disease. It is critical to identify interventions that can slow down white matter deterioration. So far, clinical trials have failed to demonstrate the benefits of aerobic exercise on the adult white matter using diffusion Magnetic Resonance Imaging. Here, we report the effects of a 6-month aerobic walking and dance interventions (clinical trial NCT01472744) on white matter integrity in healthy older adults (n = 180, 60-79 years) measured by changes in the ratio of calibrated T1- to T2-weighted images (T1w/T2w). Specifically, the aerobic walking and social dance interventions resulted in positive changes in the T1w/T2w signal in late-myelinating regions, as compared to widespread decreases in the T1w/T2w signal in the active control. Notably, in the aerobic walking group, positive change in the T1w/T2w signal correlated with improved episodic memory performance. Lastly, intervention-induced increases in cardiorespiratory fitness did not correlate with change in the T1w/T2w signal. Together, our findings suggest that white matter regions that are vulnerable to aging retain some degree of plasticity that can be induced by aerobic exercise training. In addition, we provided evidence that the T1w/T2w signal may be a useful and broadly accessible measure for studying short-term within-person plasticity and deterioration in the adult human white matter

Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33

Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20–33, n = 51 and age 60–79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases

Studying age-related changes in white matter microstructure in healthy aging using noninvasive MRI techniques

2020 Spring.Includes bibliographical references.Age-related deterioration of the white matter (WM), such as demyelination, is an important mechanism of cognitive decline in healthy aging. Lifestyle factors can influence the course of WM aging. Most evidence have used diffusion tensor imaging (DTI) metrics, but these are not specific to myelin or axons. Therefore, in this study we compared DTI metrics to a proposed proxy of myelin content, the T1-weighted image (T1-WI) to T2-weighted image (T2-WI) ratio with respect to their ability to: detect time-by-intervention interactions, predict processing speed ability, and their correlations with each other and age. We used longitudinal data from 169 cognitively healthy older adults (60-79yrs). MRI imaging (3T Siemens Trio) included 0.9mm3 MPRAGE, 1.7×1.7x3mm3 T2w and DTI (30 diff. dir., bval= 0 and 1000s/mm2, 1.7×1.7x3mm3). T1w/T2w was calculated using internal intensity calibration. We used FSL-FDT to extract DTI metrics, focused on major WM tracts using tract-based spatial statistics in FSL. From the WM skeleton, we calculated mean values for 12 regions-of-interest. Processing speed was assessed using the Virginia Cognitive Aging Battery. Results showed that the T1w/T2w produced greater time-by-intervention interactions than DTI-FA, especially in the posterior (β=0.27, p=0.01) and anterior (β=0.33, p=0.01) limb of the internal capsule. The T1w/T2w (in the whole WM) correlated with processing speed (β=-0.13, p=0.02). T1w/T2w correlated with DTI in regions with high fiber coherence/high myelin content; and with age in regions with high myelin content. Results suggest that the T1w/T2w offers greater ability than DTI to detect short-term longitudinal changes in WM, but they seem to reflect different microstructural properties in the WM. Further research is needed to gain a better understanding of its biological underpinnings and significance

Fenómeno del niño y su huella hidrometeorológíca en la cuenca del río Pamplonita, de Norte de Santander.

El proyecto de investigación tiene como finalidad estudiar el impacto del fenómeno climatológico EL NIÑO en la hidroclimatologia de la cuenca del rio Pamplonita. Para ello, se elabora una investigación de tipo descriptivo y causal. Se realiza un análisis descriptivo de la información obtenida a fin de encontrar los efectos y variaciones ocasionados por el fenómeno y establecer una relación causal entre las variaciones de temperatura, caudal y precipitación. En los resultados se realiza un estudio del estado del arte sobre el Fenómeno EL NIÑO. Seguidamente, se estudia el impacto del fenómeno EL NIÑO sobre la hidroclimatologia de la cuenca del rio Pamplonita. Por último, se analizan correlaciones tipo teleconexión entre índices climáticos globales y variables hidroclimatológicas regionales. Los resultados obtenidos de la investigación se entregan mediante tablas, formatos prestablecidos, graficas, informes y documentos en físico y digital.PregradoIngeniero(a) Civi

Fenómeno del niño y su huella hidrometeorológica en la cuenca del río Pamplonita, de Norte de Santander.

El proyecto de investigación tiene como finalidad estudiar el impacto del fenómeno climatológico EL NIÑO en la hidroclimatologia de la cuenca del rio Pamplonita. Para ello, se elabora una investigación de tipo descriptivo y causal. Se realiza un análisis descriptivo de la información obtenida a fin de encontrar los efectos y variaciones ocasionados por el fenómeno y establecer una relación causal entre las variaciones de temperatura, caudal y precipitación. En los resultados se realiza un estudio del estado del arte sobre el Fenómeno EL NIÑO. Seguidamente, se estudia el impacto del fenómeno EL NIÑO sobre la hidroclimatologia de la cuenca del rio Pamplonita. Por último, se analizan correlaciones tipo teleconexión entre índices climáticos globales y variables hidroclimatológicas regionales. Los resultados obtenidos de la investigación se entregan mediante tablas, formatos prestablecidos, graficas, informes y documentos en físico y digital.PregradoIngeniero(a) Civi

Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health

As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20–79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin

Data_Sheet_2_Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter.docx

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20–33, n = 51 and age 60–79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.</p

Data_Sheet_1_Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter.CSV

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20–33, n = 51 and age 60–79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.</p