PLS and Functional Neuroimaging: Bias and Detection Power Across Different Resampling Schemes

Churchill, Nathan W.; Afshin-Pour, Babak; Strother, Stephen C.

doi:10.1007/978-3-319-40643-5_7

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…We randomly select a part of negative and positive samples for training SVM [8,9,8,[10][11][12][13][14][15][16][17][18],PLS [19][20][21][22][23] and LSSVM [24][25][26][27][28] classifiers and the rest for testing.…”

Section: Results and Analysismentioning

confidence: 99%

Stored-grain Pests Detection Based on SVM

Cui¹,

Xiao²,

Zhou³

et al. 2019

Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMS

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Stored grain pests detection is essential for grain management. In this paper, we have proposed a machine learning method for stored-grain pest detection. We focus on crustacean pests detection using SVM. The 20 pixels width and 20 pixels height pests and background images are directly utilized for SVM training and classification. According to the experiment results, accuracy of SVM classifier is 99.40%, which outperforms LSSVM and PLS. We then conducted an interesting experiment using synthetic pest images. We employ these synthesized data as pest samples for training SVM classifier. According to the results, the SVM classifier trained via synthetic pest images is able to detect pests in images in some cases because synthetic pest images are quite different from real pest images.

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Section: Results and Analysismentioning

confidence: 99%

Stored-grain Pests Detection Based on SVM

Cui¹,

Xiao²,

Zhou³

et al. 2019

Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMS

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“…In this study we used a data-driven contrast-based PLS approach with NPAIRS split-half cross-validation on an optimized PCA subspace, providing full model flexibility within the space of our neuroimaging data. Split-half resampling was chosen over bootstrapping and split-half stability, as PLS estimates using these methods may consequently result in an upward bias of the predicted correlations (Churchill et al, 2013 , 2014 ; Kovacevic et al, 2013 ). We chose the PCA subspace ( k = 11 ) that simultaneously maximized prediction and reproducibility, thereby controlling for bias and adaptively removing noise variability within our dataset.…”

Section: Discussionmentioning

confidence: 99%

“…The reproducibility of the two split-half brain patterns was measured as the correlation of all paired LVs, r spatial = ρ( E 1 , E 2 ), which measures the stability of the latent brain patterns across independent split-halves. The PLS analysis was executed on an adaptive optimized PC-subspace maximizing the contrast-correlation and the spatial reproducibility, as described in Churchill et al ( 2013 , 2014 ). A mathematical derivation and explanation of this procedure can be found in the Supplementary Material .…”

Section: Methodsmentioning

confidence: 99%

Brain Networks Implicated in Seasonal Affective Disorder: A Neuroimaging PET Study of the Serotonin Transporter

et al. 2017

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Background: Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption to the environmental stressor of winter in females with the short 5-HTTLPR genotype, a specific subgroup previously reported to be at increased risk for developing SAD.Methods: Nineteen females, either S' carriers (LG- and S-carriers) without SAD (N = 13, mean age 23.6 ± 3.2 year, range 19–28) or S' carriers with SAD (N = 6, mean age 23.7 ± 2.4, range 21–26) were PET-scanned with [11C]DASB during both summer and winter seasons (asymptomatic and symptomatic phase, 38 scans in total) in randomized order, defined as a 12-week interval centered on summer or winter solstice. We used a multivariate Partial Least Squares (PLS) approach with NPAIRS split-half cross-validation, to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD.Results: We identified a pattern of 5-HTT levels, distinguishing females with SAD from those without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only significant in the winter. Female S' carriers with SAD, in turn, displayed robustly increased 5-HTT levels in the ventral striatum (bilaterally), right orbitofrontal cortex, middle frontal gyrus (bilaterally), extending to the left supramarginal gyrus, left precentral gyrus and left postcentral gyrus during winter compared to female S' carriers without SAD.Limitations: The study is preliminary and limited by small sample size in the SAD group (N = 6).Conclusions: These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females with the short 5-HTTLPR genotype more vulnerable to persistent stressors like winter. The affected brain regions comprise a distributed set of areas responsive to emotion, voluntary, and planned movement, executive function, and memory. The preliminary findings provide additional insight into the neurobiological components through which the anatomical distribution of serotonergic discrepancies between individuals genetically predisposed to SAD, but with different phenotypic presentations during the environmental stressor of winter, may constitute a potential biomarker for resilience against developing SAD.

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“…Dimensions 2 and 3 accounted for 24.24% (SV = 762, p < .0001) and 6.57% (SV = 397, p < .0001) respectively. A split-half procedure was employed to assess reproducibility (Z > 1.95) of the SV and latent variables in each dimension (Churchill et al, 2016(Churchill et al, , 2013McIntosh, 2021). The correlation between network reconfiguration and cognition latent variables measures association strength (Ziegler et al, 2013).…”

Section: Plsc Describes Two Significant and Reproducible Latent Dimen...mentioning

confidence: 99%

“…To quantify the reproducibility of the SVs, we performed a split-half PLSC procedure (Churchill et al, 2016(Churchill et al, , 2013McIntosh, 2021). In each iteration, the data was split into random split-half samples, and the singular value decomposition was conducted on both halves.…”

Section: Reproducibility Of Plsc Dimensions and Loadingsmentioning

confidence: 99%

Higher general intelligence is linked to stable, efficient, and typical dynamic functional brain connectivity patterns

Ng¹,

Yu²,

Feusner³

et al. 2023

Preprint

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The capacity of the brain to adaptively and flexibly reconfigure into different network connectivity patterns may underlie general cognitive ability, or g. Network reconfiguration can be captured via assessments of dynamic connectivity (dFC), which quantifies dominant temporally recurrent connectivity patterns, or “states”, common across the population. While standard dynamic measures focus on quantifying relative time spent within states, or the probability transitioning between states, these metrics fail to capture variability between individuals present in connectivity patterns across states. Here, we provide individualized assessments of connectivity flexibility and stability over time by considering within-state pattern stability, the difference in patterns across state transitions, and how well a given state represents the “typical” state in a particular individual. We leveraged resting-state fMRI data from the large-scale Human Connectome Project and data-driven multivariate Partial Least Squares Correlation to examine emergent relationships between dynamic network properties and cognition. We found that higher g was associated with maintaining distinct states over other states, efficient reconfiguration (i.e., less pattern change during common small-magnitude state transitions such as a state to itself, and greater pattern change among rare transitions between very different states), and less reconfiguration away from population-typical patterns. These results demonstrate that higher cognitive abilities are associated with greater state-specific stability, greater connectivity differences when transitioning between distinct states, and reconfiguration into more typical, potentially optimal, connectivity patterns within states. This suggests a link between general cognition and the efficiency of reconfiguration connectivity patterns into stable, well-defined, and typical network states.Significance StatementCognitive performance can be summarized by a general capability - “g”. g has been associated with academic and professional achievement and is critical in a world where higher-order cognition is increasingly necessary to excel. Attempts to improve g are hampered by a lack of mechanistic understanding. A recent theory suggests that the capacity to reconfigure brain connectivity drives g. We investigate metrics which characterize switches in recurring dominant connectivity patterns and propose novel metrics which investigate connectivity changes during the switches and connectivity pattern deviation from the population. Our findings suggest that g is associated with the maintenance of special patterns, greater pattern changes when necessary, and having population-typical patterns. This advances our understanding of g and informs future interventions.

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PLS and Functional Neuroimaging: Bias and Detection Power Across Different Resampling Schemes

Cited by 3 publications

References 8 publications

Stored-grain Pests Detection Based on SVM

Stored-grain Pests Detection Based on SVM

Brain Networks Implicated in Seasonal Affective Disorder: A Neuroimaging PET Study of the Serotonin Transporter

Higher general intelligence is linked to stable, efficient, and typical dynamic functional brain connectivity patterns

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