ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH
Vol. 42, No. 5 May 2018
Familial Risk for Alcohol Dependence and Brain Morphology: The Role of Cortical Thickness Across the Lifespan Shirley Y. Hill
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ENDERSON AND COLLEAGUES (2018) have provided new data from a large cohort demonstrating that cortical thickness, as one index of brain morphology, differs between adolescents with and without a family history of alcohol dependence. Understanding the relationship between brain structure and cognitive ability that differ in those with a family history of alcohol use disorders (AUD) and those without relatives with AUD may provide clues about the biological substrate of addiction. To date, most of the studies concerning brain morphological differences by familial risk have focused on volumetric differences. These studies of morphological differences have revealed that, compared to healthy controls from low-risk families, child/adolescent and young adult offspring with a family history of AUD show alterations in volume. Several brain regions have been reported to show reduction in volume in offspring with a family history of alcohol dependence including the right hemisphere of the orbitofrontal cortex (Hill et al., 2009, 2010), the caudate nucleus, when externalizing disorders are present (Benegal et al., 2007; Hill et al., 2013a), and the amygdala (Benegal et al., 2007; Dager et al., 2015; Hill et al., 2001, 2013b). Significantly larger volume has also been reported for total cerebellum volume in high-risk offspring (Hill et al., 2011) and for regions of the cerebellum (Hill et al., 2016). The larger volume may reflect developmental delay in gray matter pruning. Additionally, the nucleus accumbens has been reported to be influenced by the family density of alcoholism with positive associations seen in drug and alcohol na€ıve female adolescent offspring from families with alcohol dependence (Cservenka et al., 2015). These results have been observed in samples where either the majority of cases had not yet developed a substance use disorder (Dager et al., 2015; Hill et al., 2001), participants were alcohol-na€ıve (Benegal et al., 2007), or the reduction in From the Department of Psychiatry (SYH), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Received for publication January 4, 2018; accepted March 5, 2018. Reprint requests: Shirley Y. Hill, PhD, Department of Psychiatry, University of Pittsburgh Medical Center, 3811 O’ Hara St. Pittsburgh, PA 15213; Tel.: 412-624-3505; Fax: 412-624-3986; E-mail:
[email protected] Copyright © 2018 by the Research Society on Alcoholism.
volume was seen even when cases with substance use disorder were removed (Hill et al., 2009, 2013a,b). These observations suggest that the morphological differences observed may antedate the development of AUD and reflect an underlying genetic susceptibility. Recently, it has been shown that morphological variation between high and low-risk offspring with and without family histories of alcohol dependence may be associated with later development of substance use disorders (O’Brien and Hill, 2017). Many of the studies investigating potential differences between high and low-risk offspring have focused on candidate regions as reviewed here. A few studies have investigated global brain changes through voxel-based morphometry. Contrasting high and low-risk offspring for alcohol dependence, we noted reduced volume in regions that were separate from those seen in association with prenatal exposure to alcohol and other drugs (Sharma and Hill, 2017). For familial risk, we found that high-risk male subjects exhibited lower gray matter volume in the fusiform, insula, and inferior temporal regions. Similarly, female high-risk subjects showed reduced volume for the right fusiform, a region that has been identified as important in social cognition. The Henderson and colleagues (2018) study is one of the first global investigations of cortical thickness and subregions using analyses performed with FreeSurfer, a powerful algorithm for uncovering variation between samples. Among the strengths of the study is the relatively large number of participants (N = 188) studied across adolescence (ages 13 to 18). The principal finding of their study is that FH+ adolescents in comparison with those who are without a family history (FH ) have thinner cortices in frontal and parietal lobes. These included the medial orbitofrontal, lateral orbitofrontal, and superior parietal regions. The question then becomes what does this relative reduction in cortical thickness mean for cognitive functioning and/or risk for developing an AUD? As has frequently been observed (Hill and O’Brien, 2015; Tessner and Hill, 2010), Henderson and colleagues (2018) report that FH+ adolescents were more impulsive and performed more poorly on selected neuropsychological tests that were administered. An extensive battery of tests covering spatial working memory and executive functioning was included in the study (Trail Making Test Parts A and B,
DOI: 10.1111/acer.13621 Alcohol Clin Exp Res, Vol 42, No 5, 2018: pp 841–844
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Digit Span [Forward and Backward], Letter Fluency, Category Fluency and Visuospatial Sequencing [VST] [Forward and Reverse]). Impulsivity was assessed using the Delay Discounting Task (DDT). Performance was worse for the FH+ adolescents for Trails B and VST (Forward and Reverse). Henderson and colleagues (2018) tested for possible associations between neuropsychological performance and cortical thickness. Significant correlations were not found when the entire sample was analyzed together, or when the FH+ were considered alone. However, significant correlations were reported for the FH subjects. Greater time spent completing the Trails indicates poorer performance while greater scores on the VST indicate better performance. Right frontal lobe thickness showed a positive correlation with time to complete Trails B; the greater the thickness of the right frontal lobe, the greater the time spent completing the Trails indicating poorer performance. Left parietal thickness was negatively correlated with the VST score. These results are consistent with reports that lesser cortical thickness is associated with better performance on tests of general intelligence in children, adolescents, and young adults (Menary et al., 2013). With the developmental goal being to achieve a thinner cortex with maturation, it might be expected that those with more advanced maturation might perform better on tests of intelligence. Similarly, early adolescent cortical thinning is seen in association with better neuropsychological performance (Squeglia et al., 2013). A comment is needed with respect to the differing results found for the FH and FH+ groups for the correlation between cortical thickness and neuropsychological functioning. The authors suggest that FH+ adolescents may have an altered structure–function relationship from that of the FH adolescents. While this may be a plausible assumption, it cannot be concluded from this study. The functional differences reported were not especially strong with only 3 of 8 tests showing significant risk group differences. False discovery correction when applied to the significance levels reported does not reveal significance. It is unclear why functional differences between offspring from the differing familial risk groups were not seen. Perhaps, the adolescents were not sufficiently challenged by the neuropsychological tests used so that the relationship between neuropsychological performance and cortical thickness may not have been completely evaluated. The results for the cortical thickness relationship with impulsive behavior seen in the FH group appear to provide a stronger association. The DDT was administered to both FH+ and FH groups. The DDT requires subjects to choose between a hypothetical monetary reward that is immediately available and a larger reward available at a later time. The FH+ adolescents had significantly greater DDT scores indicating greater impulsivity. The DDT results were then correlated with cortical thickness. A marginally significant negative correlation between cortical thickness and DDT performance was seen. Because a single test of impulsive behavior was administered, removing the necessity of
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adjustment for multiple comparisons, this result, albeit marginally significant, does suggest that cortical thickness of the parietal lobe may provide the structural underpinning for impulsive behavior as revealed in the normal controls. With change in cortical thickness seen in normal development, the Henderson and colleagues (2018) results further our understanding of how maturation of brain structure contributes to development of cognitive control. The lack of correlation in the FH+ group may be the result of greater variation in cortical thickness and performance on the DDT among those with a family history of alcohol dependence. Because not all adolescents who are FH+ will later develop AUD or a related substance use disorder, it may be expected that quantitative variation in the predictor of interest, here the DDT, might be evident among FH+ individuals, potentially weakening the observed correlation reported. One aspect of the study by Henderson and colleagues (2018) is clear; cortical thickness is reduced in the frontal and parietal areas in FH+ youth. However, the absence of a significant relationship between cognitive functioning and cortical thickness in the whole sample of adolescents presents challenges for understanding how the process of thinning may impact cognitive performance and, perhaps, the risk for developing AUD. The findings from this study present an opportunity to consider what may be the most optimal strategy for identifying structural underpinnings for functional differences, including neuropsychological performance, that are frequently reported in comparisons between individuals with and without a family history of alcohol dependence. A steady decline in cortical thickness is seen in adulthood reflecting age-related changes in the human brain (Storsve et al., 2014) that are accompanied by a decline in volume most notably in the temporal region (Pfefferbaum et al., 2013). Decreased volume and cortical thickness that accompany aging appear to provide the neural substrate for the neuropsychological decline that accompanies advancing age. Congruently, cortical thickness and IQ are positively associated in adults (Narr et al., 2007). Based on results from a large sample of healthy 9- to 24year-olds, negative associations between cortical thickness and the block design test from the Wechsler Abbreviated Scales of Intelligence were seen in the younger portion of the sample but positive associations seen in the older group (Menary et al., 2013). Similarly, Schnack and colleagues (2015) found that more intelligent children tended to have a thinner cortex, but by age 42, a thicker cortex was associated with higher intelligence. In short, cortical thinning that is associated with impaired cognitive functioning in adults is associated with better cognitive functioning in childhood and adolescence. Although a good case can be been made for cortical thickness as one metric of functional differences between groups of individuals, it may not be the ideal measure for predicting functional effects. Review of studies that have reported on the relationship between cortical thickness and IQ find a
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significant positive relationship in just half of the reports (Vuoksimaa et al., 2015). Possibly cortical volume or surface area may offer a closer match to function. Recently, it has been suggested that cortical surface might be a more fruitful direction for understanding general cognitive ability because it is most related to higher intellectual abilities (Fjell et al., 2013). Historically, structural assessments have concentrated on volume as the primary metric of inquiring using manual tracing techniques that sum the 2-dimensional measures of area into a 3-dimensional volume assisted by interactive software such as BRAINS2 (Magnotta et al., 2002) or NIH IMAGE (Schneider et al., 2012). Voxel-based morphometry also provides an estimate of whole brain and region-specific volumes but is dependent on direct statistical comparison of 2 groups using clusters of voxels that differ between groups. The potential for comparing metrics (volume, cortical thickness, and surface area) within the same study has only been possible with the development of tools such as FreeSurfer that provides all 3 metrics. Cognitive ability and brain structure are both highly heritable (Schmitt et al., 2007). Using Vietnam era middle-aged veteran twins (N = 534) (monozygotic and dizygotic) with magnetic resonance imaging scans, Vuoksimaa and colleagues (2015) set out to evaluate the phenotypic and genetic relationships between cortical volume and generalized cognitive ability. These relationships were then decomposed into neocortical measures of cortical thickness and surface area and their relationship with general cognitive ability. Analyses of the genetic and environmental contributions to the similarity in cortical thickness and surface observed suggest that cortical thickness shows lesser genetic variance than cortical gray matter volume, which is a product of cortical surface and thickness (Vuoksimaa et al., 2015). Because of its lesser genetic determination, it may not be surprising that cortical thickness measured in childhood has a more modest relationship to general cognitive ability than it does in adulthood where cortical thinning is more often related to neurotoxic exposures. Further analysis of specific cognitive abilities in the Vuoksimaa and colleagues (2015) study revealed significant correlations between surface area and verbal ability, arithmetic scores, and spatial processing. Cortical thickness showed a significant correlation for just arithmetic performance. It appears that even in adult samples, volumetric measures and surface area may be more related to cognitive abilities. The intriguing question is why volume or surface area measures appear to predict cognitive functioning better than does cortical thickness. Thinning may be due to different neurobiological processes at different ages (Walhovd et al., 2016) requiring specification of the level of cognitive functioning and the age of participants who are scanned. In children and adolescents, thickness may not be correlated with cognitive functioning because brain volume could possibly be the result of processes occurring early in neurodevelopment when there are active pruning and reorganization
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of white matter tracts accompanied by increased expansion of the brain. All of these processes may facilitate connectivity required for efficient cognitive processing. In adults, environmental exposures may primarily affect volume due to an overall neuropathological effect on neurons. Cortical surface area is distinct from cortical thickness genetically (Pannizon et al., 2009) and appears also to be distinct during development (Rakic, 1988) suggesting inclusion of this important metric in studies designed to assess potential structural and functional differences between FH+ and FH in the future studies. Also, it is worth noting that the magnitude and timing of brain changes may be more related to cognitive functioning than structure per se (Schnack et al., 2015). In summary, the Henderson and colleagues (2018) study provides new and important data on global aspects of morphological differences between offspring differing by family history of alcohol dependence finding reduced cortical thickness in frontal and parietal regions in those with a family history of alcohol dependence relative to those without such history. Interpretation of the Henderson and colleagues (2018) findings is challenging because having a thinner cortex appears advantageous in childhood/adolescence with better cognitive performance (higher IQ) associated with lesser cortical thickness reported in several studies. An additional difficulty is the lack of significant correlations in the whole sample for brain morphology and neuropsychological performance. Finally, although the FH+ group displayed greater impulsive behavior than the FH controls, this was not significantly related to cortical thickness. CONFLICT OF INTEREST The author declares no conflict of interest. REFERENCES Benegal V, Antony G, Venkatasubramanian G, Jayakumar PN (2007) Gray matter volume abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addict Biol 12:122–132. Cservenka A, Gillespie AJ, Michael PG, Nagel BJ (2015) Family history density of alcoholism relates to left nucleus accumbens volume in adolescent girls. J Stud Alcohol Drugs 76:47–56. Dager AD, McKay DR, Kent JW Jr, Curran JE, Knowles E, Sprooten E, G€ oring HH, Dyer TD, Pearlson GD, Olvera RL, Fox PT, Lovallo WR, Duggirala R, Almasy L, Blangero J, Glahn DC (2015) Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacology 40:412–420. Fjell AM, Westlye LT, Amlien I, Tamnes CK, Grydeland H, Engvig A, Espeseth T, Reinvang I, Lundervold AJ, Lundervold A, Walhovd KB (2013) High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cereb Cortex 25:26–34. Henderson KE, Vaidya JG, Kramer JR, Kuperman S, Langbehn DR, O’Leary DS (2018) Cortical thickness in adolescents with a family history of alcohol use disorder. Alcohol Clin Exp Res 42:89–99. Hill SY, DeBellis MD, Keshavan MS, Lowers L, Shen S, Hall J, Pitts T (2001) Right amygdala volume in adolescent/young adult offspring from families at high risk for developing alcoholism. Biol Psychiatry 49: 894–905.
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