Functional differentiation in the human ventromedial frontal lobe: A data‐driven parcellation

Abstract

Ventromedial regions of the frontal lobe (vmFL) are thought to play a key role in decision‐making and emotional regulation. However, aspects of this area's functional organization, including the presence of a multiple subregions, their functional and anatomical connectivity, and the cross‐species homologies of these subregions with those of other species, remain poorly understood. To address this uncertainty, we employed a two‐stage parcellation of the region to identify six distinct structures within the region on the basis of data‐driven classification of functional connectivity patterns obtained using the meta‐analytic connectivity modeling (MACM) approach. From anterior to posterior, the derived subregions included two lateralized posterior regions, an intermediate posterior region, a dorsal and ventral central region, and a single anterior region. The regions were characterized further by functional connectivity derived using resting‐state fMRI and functional decoding using the Brain Map database. In general, the regions could be differentiated on the basis of different patterns of functional connectivity with canonical “default mode network” regions and/or subcortical regions such as the striatum. Together, the findings suggest the presence of functionally distinct neural structures within vmFL, consistent with data from experimental animals as well prior demonstrations of anatomical differences within the region. Detailed correspondence with the anterior cingulate, medial orbitofrontal cortex, and rostroventral prefrontal cortex, as well as specific animal homologs are discussed. The findings may suggest future directions for resolving potential functional and structural correspondence of subregions within the frontal lobe across behavioral contexts, and across mammalian species.

Obesity is associated with reduced orbitofrontal cortex volume: A coordinate-based meta-analysis

“…Macaques exposed to an ‘obesiogenic’ diet, which offered calorically dense food in addition to chow versus those offered chow only (healthy diet) in a randomized controlled trial, gained significantly more weight over a year and exhibited decreased orbitofrontal cortex (BA 11 and 13) to nucleus accumbens functional connectivity as well as greater peripheral inflammation (C-reactive protein), ( Godfrey et al, 2018 0; Godfrey et al, 2020 ). In this trial, stress appeared to have an interactive effect with the obesiogenic diet unlike the chow-only diet by not resulting in Dopamine 2 receptor binding in this orbitofrontal cortex (BA 11 and 13) region, suggesting that stress and regulation of stress ( Morawetz et al, 2020 , Chase et al, 2020 ) may further disrupt orbitofrontal cortex functioning. Human controlled trials of diet interventions examining neural response to palatable food pictures or high-fat high-sweet food have also shown that weight loss is associated with change in orbitofrontal cortex activity ( Murdaugh et al, 2012 , Chen et al, 2017 ).…”

“…Whole-brain voxel-based morphometry studies were included in the primary analysis to confirm differences between individuals in the overweight and obese body mass index range relative to those in the healthy body mass index range ( Centers of Disease Control, 2020 ). Large-scale connectivity meta -analyses have shown that brain regions do not typically act in isolation but rather in a network with other regions ( Alves et al, 2019 , Riedel et al, 2018 , de la Vega et al, 2016 , van den Heuvel and Sporns, 2013 , Morawetz et al, 2020 , Chase et al, 2020 ). Given this, we next explored the brain regions that are typically coactive with the areas associated with body mass index that were identified in our primary meta -analysis.…”

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate

“…Second, we used the k -means clustering algorithm to divide the human and macaque striatum voxels into specified number of k non-overlapping clusters based on the RSFC pattern of each striatal voxel. The k -means clustering algorithm has been successfully employed in several CBP studies investigating different brain regions and modalities ( Chase et al., 2020 ; Crippa et al., 2011 ; Genon et al., 2018 ; Hartwigsen et al., 2019 ; Jung et al., 2014 ; Pauli et al., 2016 ; Plachti et al., 2019b ; Ray et al., 2015 ; Reuter et al., 2020 ; Xu et al., 2020a ) and is known to provide highly accurate solutions ( Thirion et al., 2014 ). However, it is also known that k -means algorithm can be unstable and provide sub-optimal solutions.…”

Goal setting and attaining: Neural correlates of positive coping style and hope

“…In a recent study (Chase et al., 2020), it was found that FPC is a subregion of ventromedial frontal lobe (vmFL). Studies have identified an increase in ALFF values in the medial prefrontal cortex (mPFC) in patients with schizophrenia (Alonso‐Solís et al., 2017; Xu et al., 2015); meanwhile, increased vmFL activity may lead to poor sertraline treatment outcome in late‐life depression.…”

“…The study conducted by Wang et al (2017) revealed that the brain areas involved in hope are mainly associated with the medial orbitofrontal cortex (mOFC). However, recent studies (Chase et al, 2020;Yucel et al, 2014) have shown that the location of the voxel cluster, as claimed by Wang et al (2017), shows a significant correlation between brain region and trait hope, which would more likely involve the frontal pole cortex (FPC) rather than the mOFC. Thus, this study argues that hope is linked to amplitude of low-frequency fluctuation (ALFF)/fractional ALFF (fALFF) in FPC, a subdivision of the prefrontal cortex (PFC) associated with problem-solving, planning, and executive functions (Hosoda et al, 2020;Raju et al, 2021).…”