Savings in CT Net Scan Energy Consumption: Assessment Using Dose Report Metrics and Comparison With Savings in Idle State Energy Consumption.

BACKGROUND. CT scanners' net scan state (i.e., image acquisition period) represents a potential target for energy savings through protocol adjustments. However, gauging CT energy savings is difficult without installing costly energy monitors. OBJECTIVE. The purpose of this article was to assess correlations between CT dose report metrics and energy consumption during the system net scan state and to compare theoretic energy savings from matching percentage reductions in energy consumption during net scan and idle system states. METHODS. Current sensors were installed on a single CT scanner. A phantom was scanned at varying kilovoltage settings and effective tube current-rotation time settings. A retrospective assessment was performed in 32 patients (mean age, 61.2 ± 17.9 [SD] years; 17 men, 15 women) who underwent 32 single-energy noncontrast abdominopelvic CT examinations from September 22, 2021, to September 27, 2021, on the same scanner. Correlations between dose report metrics and net scan energy consumption were assessed in the phantom and clinical scans, and equations were generated to derive net scan energy consumption from DLP. An additional retrospective assessment was performed in 1355 patients (mean age, 59.3 ± 16.9 years; 663 men, 692 women) who underwent 1728 single-energy noncontrast abdominopelvic CT examinations from January 1, 2021, through December 31, 2021, on the same scanner to estimate net scan energy consumption per examination. This information was integrated with literature-derived values to compare estimated annual national energy savings resulting from 20% reductions in net scan and idle state energy consumption. RESULTS. Net scan energy consumption in the phantom scans showed high linear correlation with DLP (R2 = 0.87), and, in the clinical scans, high linear correlation with CTDIvol (R2 = 0.89) and very high linear correlation with DLP (R2 = 0.92). When combining mean DLP in examinations performed in the 1-year interval, an equation relating DLP and net scan energy consumption and literature values estimated that annual national energy savings was 14.9 times greater (40,437,870 kWh/2,704,000 kWh) by targeting the idle state rather than net scan state. CONCLUSION. CT net scan energy savings can be inferred from reductions in dose report metrics. However, targeting net scan energy consumption has modest impact relative to targeting idle state energy consumption. CLINICAL IMPACT. Environmental sustainability efforts should target the idle state energy consumption of CT.

Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.

Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.

Neuroanatomical differences in Latinx children from rural farmworker families and urban non-farmworker families and related associations with pesticide exposure.

Exposure to pesticides in humans may lead to changes in brain structure and function and increase the likelihood of experiencing neurodevelopmental disorders. Despite the potential risks, there is limited neuroimaging research on the effects of pesticide exposure on children, particularly during the critical period of brain development. Here we used voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) from magnetic resonance images (MRI) to investigate neuroanatomical differences between Latinx children (n = 71) from rural, farmworker families (FW; n = 48) and urban, non-farmworker families (NFW; n = 23). Data presented here serves as a baseline for our ongoing study examining the longitudinal effects of living in a rural environment on neurodevelopment and cognition in children. The VBM analysis revealed that NFW children had higher volume in several distinct regions of white matter compared to FW children. Tract-based spatial statistics (TBSS) of DTI data also indicated NFW children had higher fractional anisotropy (FA) in several key white matter tracts. Although the difference was not as pronounced as white matter, the VBM analysis also found higher gray matter volume in selected regions of the frontal lobe in NFW children. Notably, white matter and gray matter findings demonstrated a high degree of overlap in the medial frontal lobe, a brain region predominantly linked to decision-making, error processing, and attention functions. To gain further insights into the underlying causes of the observed differences in brain structure between the two groups, we examined the association of organochlorine (OC) and organophosphate (OP) exposure collected from passive dosimeter wristbands with brain structure. Based on our previous findings within this data set, demonstrating higher OC exposure in children from non-farmworker families, we hypothesized OC might play a critical role in structural differences between NFW and FW children. We discovered a significant positive correlation between the number of types of OC exposure and the structure of white matter. The regions with significant association with OC exposure were in agreement with the findings from the FW-NFW groups comparison analysis. In contrast, OPs did not have a statistically significant association with brain structure. This study is among the first multimodal neuroimaging studies examining the brain structure of children exposed to agricultural pesticides, specifically OC. These findings suggest OC pesticide exposure may disrupt normal brain development in children, highlighting the need for further neuroimaging studies within this vulnerable population.

Sinonasal organized hematoma: Case report and review of the literature.

Sinonasal organized hematoma is a rare condition characterized by an organizing blood clot in the sinonasal cavity, which consists of blood products, fibrin, and dilated blood vessels. As a benign entity with an aggressive imaging appearance, it is important to differentiate sinonasal organized hematoma from malignancies affecting the paranasal sinuses and nasal cavities to guide appropriate management. In this report, we discuss the clinical presentation and diagnostic evaluation of an 82-year-old male with a left maxillary sinus organized hematoma and provide a comprehensive review of the relevant literature.

Associations of physical function and body mass index with functional brain networks in community-dwelling older adults.

Deficits in physical function that occur with aging contribute to declines in quality of life and increased mortality. There has been a growing interest in examining associations between physical function and neurobiology. Whereas high levels of white matter disease have been found in individuals with mobility impairments in structural brain studies, much less is known about the relationship between physical function and functional brain networks. Even less is known about the association between modifiable risk factors such as body mass index (BMI) and functional brain networks. The current study examined baseline functional brain networks in 192 individuals from the Brain Networks and mobility (B-NET) study, an ongoing longitudinal, observational study in community-dwelling adults aged 70 and older. Physical function and BMI were found to be associated with sensorimotor and dorsal attention network connectivity. There was a synergistic interaction such that high physical function and low BMI were associated with the highest network integrity. White matter disease did not modify these relationships. Future work is needed to understand the causal direction of these relationships.

Comparing impact of pesticide exposure on cognitive abilities of Latinx children from rural farmworker and urban non-farmworker families in North Carolina.

Pesticide exposure remains a health hazard despite extensive study into adverse effects. Children in vulnerable populations, such as Latinx children in farmworker families, are particularly at risk for exposure. Several studies have demonstrated the detrimental cognitive effects of prenatal exposure to pesticides, particularly organophosphates (OPs) within this high-risk group. However, results from studies investigating the cognitive effects of early childhood pesticide exposure are equivocal. Most studies examining the effects of pesticide exposure have used correlative analyses rather than examining populations with expected high and low exposure. The current study compares 8-year-old children from rural families of farmworkers and urban, non-farmworker families. We used the Weschler Intelligence Scale for Children - Fifth Edition (WISC-V) to assess cognitive performance in these children. We designed this study with the expectation that children from farmworker families would have greater exposure to agricultural pesticides than urban, non-farmworker children. This assumption of exposure to agricultural pesticides was confirmed in a recent report that assessed exposure probabilities using life history calendars. However, data from passive wristband sampling of acute (1-week) pesticide exposure from these same children indicate that both study populations have considerable pesticide exposure but to different chemicals. As expected the children of farmworkers had greater OP exposure than non-farmworker children, but the non-farmworker children had greater exposure to two other classes of insecticides (organochlorines [OCs] and pyrethroids). Our analyses considered these findings. A comparison of the cognitive scores between groups revealed that children from farmworker families had slightly higher performance on the Visual-Spatial Index (VSI) and Verbal Comprehension Index (VCI) when compared to children from non-farmworker families. Regression analyses where pesticide exposure was included as covariates revealed that OC exposure accounted for the largest portion of the group differences for both VSI and VCI. However, a post-hoc moderation analysis did not find significant interactions. The main study outcome was that the non-farmworker children exhibited lower WISC-V scores than the children from farmworker families, and the analyses incorporating pesticide exposure measures raise the hypothesis the that pervasive and persistent nature of a variety of pesticides may have adverse effects on the neurodevelopment of young Latinx children whether living in rural or non-farmworker environments.

Altered default mode network associated with pesticide exposure in Latinx children from rural farmworker families.

Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.

Longitudinal relationship of baseline functional brain networks with intentional weight loss in older adults.

OBJECTIVE: The goal of this study was to determine whether the degree of weight loss after 6 months of a behavior-based intervention is related to baseline connectivity within two functional networks (FNs) of interest, FN1 and FN2, in a group of older adults with obesity. METHODS: Baseline functional magnetic resonance imaging data were collected following an overnight fast in 71 older adults with obesity involved in a weight-loss intervention. Functional brain networks in a resting state and during a food-cue task were analyzed using a mixed-regression framework to examine the relationships between baseline networks and 6-month change in weight. RESULTS: During the resting condition, the relationship of baseline brain functional connectivity and network clustering in FN1, which includes the visual cortex and sensorimotor areas, was significantly associated with 6-month weight loss. During the food-cue condition, 6-month weight loss was significantly associated with the relationship between baseline brain connectivity and network global efficiency in FN2, which includes executive control, attention, and limbic regions. CONCLUSION: These findings provide further insight into complex functional circuits in the brain related to successful weight loss and may ultimately aid in developing tailored behavior-based treatment regimens that target specific brain circuitry.

Children with chronic nausea and orthostatic intolerance have unique brain network organization: A case-control trial.

BACKGROUND: Determine whether subjects with chronic nausea and orthostatic intolerance share common alterations in key brain networks associated with central autonomic control: default mode, salience, and central executive networks, and the insula, a key component of the salience network. METHODS: Ten subjects (ages 12-18 years; 8 females, 2 males) with nausea predominant dyspepsia, orthostatic intolerance, and abnormal head-upright tilt test were consecutively recruited from pediatric gastroenterology clinic. These subjects were compared with healthy controls (n = 8) without GI symptoms or orthostatic intolerance. Resting-state fMRI and brain network modularity analyses were performed. Differences in the default mode, salience, and central executive networks, and insular connectivity were measured. KEY RESULTS: The community structure of the default mode network and salience network was significantly different between tilt-abnormal children and controls (p = 0.034 and 0.012, respectively), whereas, no group difference was observed in the central executive network (p = 0.48). The default mode network was more consistently "intact," and the consistency of the community structure in the salience network was reduced in tilt-abnormal children, especially in the insula. CONCLUSIONS AND INFERENCES: Children with chronic nausea and orthostatic intolerance have altered connectivity in the default mode network and salience network/insula, which supports over-monitoring of their body and altered processing of bodily states resulting in interoceptive hyper self-awareness. The connectivity of the salience network would not support optimal regulation of appropriate attention to internal and external stimuli, and the hyper-connected default mode network may result in a persistent self-referential state with feelings of emotion, pain, and anxiety.

Aging and Neural Vulnerabilities in Overeating: A Conceptual Overview and Model to Guide Treatment.

Given the vulnerability of older adults to chronic disease and physical disability, coupled with the threat that obesity poses to healthy aging, there is an urgent need to understand the causes of positive energy balance and the struggle that many older adults face with intentional weight loss. This paper focuses on neural vulnerabilities related to overeating in older adults, and moderating variables that can have either favorable or unfavorable effect these vulnerabilities. Research from our laboratory on older adults with obesity suggests that they are prone to similar neural vulnerabilities for overeating that have been observed in younger and middle-aged populations. In addition, following brief postabsorptive states, functional brain networks both in the resting state and in response to active imagery of desired food are associated with 6-month weight loss. Data reviewed suggest that the sensorimotor network is a central hub in the process of valuation and underscores the central role played by habits in overeating. Finally, we demonstrate how research on the neural vulnerabilities for overeating offers a useful framework for guiding clinical decision-making in weight management.

Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study.

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool-short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques.

Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning. Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics. Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly. Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.

Therapeutic Instrumental Music Training and Motor Imagery in Post-Stroke Upper-Extremity Rehabilitation: A Randomized-Controlled Pilot Study.

OBJECTIVE: To investigate the potential benefits of 3 therapeutic instrumental music performance (TIMP)-based interventions in rehabilitation of the affected upper-extremity (UE) for adults with chronic poststroke hemiparesis. DESIGN: Randomized-controlled pilot study. SETTING: University research facility. PARTICIPANTS: Community-dwelling volunteers (N=30; 16 men, 14 women; age range, 33-76 years; mean age, 55.9 years) began and completed the protocol. All participants had sustained a unilateral stroke more than 6 months before enrollment (mean time poststroke, 66.9 months). INTERVENTION: Two baseline assessments, a minimum of 1 week apart; 9 intervention sessions (3 times/week for 3 weeks), in which rhythmically cued, functional arm movements were mapped onto musical instruments; and 1 post-test following the final intervention. Participants were block-randomized to 1 of 3 conditions: group 1 (45 minutes TIMP), group 2 (30 minutes TIMP, 15 minutes metronome-cued motor imagery [TIMP+cMI]), and group 3 (30 minutes TIMP, 15 minutes motor imagery without cues [TIMP+MI]). Assessors and investigators were blinded to group assignment. MAIN OUTCOME MEASURES: Fugl-Meyer Upper-Extremity (FM-UE) and Wolf Motor Function Test- Functional Ability Scale (WMFT-FAS). Secondary measures were motor activity log (MAL)-amount of use scale and trunk impairment scale. RESULTS: All groups made statistically significant gains on the FM-UE (TIMP, P=.005, r=.63; TIMP+cMI, P=.007, r=.63; TIMP+MI, P=.007, r=.61) and the WMFT-FAS (TIMP, P=.024, r=.53; TIMP+cMI, P=.008, r=.60; TIMP+MI, P=.008, r=.63). Comparing between-group percent change differences, on the FM-UE, TIMP scored significantly higher than TIMP+cMI (P=.032, r=.57), but not TIMP+MI. There were no differences in improvement on WMFT-FAS across conditions. On the MAL, gains were significant for TIMP (P=.030, r=.54) and TIMP+MI (P=.007, r=.63). CONCLUSION: TIMP-based techniques, with and without MI, led to significant improvements in paretic arm control on primary outcomes. Replacing a physical training segment with imagery-based training resulted in similar improvements; however, synchronizing internal and external cues during auditory-cMI may pose additional sensorimotor integration challenges.

Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity.

OBJECTIVE: The purpose of this study was to determine whether baseline measures of hedonic hunger-the Power of Food Scale-and self-control for food consumption-the Weight Efficacy Lifestyle Questionnaire-were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure. METHODS: Baseline functional magnetic resonance imaging data (resting state and food cue task) were collected in a novel cohort of 67 older adults with obesity (65-85 years, BMI = 35-42 kg/m2 ) participating in an 18-month randomized clinical trial on weight regain. RESULTS: The Power of Food Scale was most related to ROI 1, which includes the visual cortex and sensorimotor processing areas during only the food cue state. During both the food cue and resting conditions, the Weight Efficacy Lifestyle Questionnaire was associated with ROI 2, which includes areas of the attention network and limbic circuitry. CONCLUSIONS: Our findings show critical, distinct links between brain network topology with self-reported measures that capture hedonic hunger and the confidence that older adults have in resisting the consumption of food because of both intrapersonal and social/environmental cues.

Transitioning from Radiology Training to Academic Faculty: Defining Your Role and Interests.

Transitioning from radiology training to academic faculty presents many challenges. In this review, we discuss strategies to navigate this process and to facilitate success through appropriate selection of career tracks. Various modern avenues include roles as a Clinician-Educator, Clinician-Investigator, and Clinician-Administrator. Selection of the appropriate career track based on personal interests and institutional culture is critical for early and long-term career satisfaction.

Transitioning From Radiology Training to Academic Faculty: The Importance of Mentorship.

Transitioning from radiology residency to academic faculty presents many challenges. In this review, we discuss the importance of introspection and mentorship to successfully navigate this process. Key points include alignment of goals with those of the institution, formation of a mentorship program, and periodic reassessment of career goals. These tips and tools can help make the transition from residency to academic faculty more seamless.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.

As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.

Functional Brain Network Changes Following Use of an Allostatic, Closed-Loop, Acoustic Stimulation Neurotechnology for Military-Related Traumatic Stress.

BACKGROUND AND PURPOSE: Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM® ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress. METHODS: Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sample statistical tests. RESULTS: Postintervention, there was an overall increase in connectivity of the default mode network (P = .0094). There were decreases of community structure in both the anterior portion of the default mode (medial prefrontal cortex, P = .0097) and in the sensorimotor (P = .005) network. There were no statistically significant changes at the whole brain level, or in the central executive, salience, or other networks analyzed. Participants demonstrated significant improvements in clinical symptoms, as well as autonomic cardiovascular regulation, which have been reported previously. CONCLUSIONS: Use of closed-loop, allostatic, acoustic stimulation neurotechnology (HIRREM) was associated with connectivity changes in the default mode and sensorimotor networks, in directions that may have explained the subjects' clinical improvements.

Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment.