Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Anorexia Nervosa during Adolescence Is Associated with Decreased Gray Matter Volume in the Inferior Frontal Gyrus

  • Takashi X. Fujisawa ,

    Contributed equally to this work with: Takashi X. Fujisawa, Chiho Yatsuga

    Affiliations Research Center for Child Mental Development, University of Fukui, Fukui, Japan, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Japan

  • Chiho Yatsuga ,

    Contributed equally to this work with: Takashi X. Fujisawa, Chiho Yatsuga

    Affiliations Research Center for Child Mental Development, University of Fukui, Fukui, Japan, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Japan

  • Hiroyo Mabe,

    Affiliation Department of Child Development, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan

  • Eiji Yamada,

    Affiliation Department of Child Development, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan

  • Masato Masuda,

    Affiliation Department of Child Development, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan

  • Akemi Tomoda

    Affiliations Research Center for Child Mental Development, University of Fukui, Fukui, Japan, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Japan


Anorexia nervosa (AN) is an eating disorder characterized by the relentless pursuit to lose weight, mostly through self-starvation, and a distorted body image. AN tends to begin during adolescence among women. However, the underlying neural mechanisms related to AN remain unclear. Using voxel-based morphometry based on magnetic resonance imaging scans, we investigated whether the presence of AN was associated with discernible changes in brain morphology. Participants were 20 un-medicated, right-handed patients with early-onset AN and 14 healthy control subjects. Group differences in gray matter volume (GMV) were assessed using high-resolution, T1-weighted, volumetric magnetic resonance imaging datasets (3T Trio scanner; Siemens AG) and analyzed after controlling for age and total GMV, which was decreased in the bilateral inferior frontal gyrus (IFG) (left IFG: FWE corrected, p < 0.05; right IFG: uncorrected, p < 0.05) of patients with AN. The GMV in the bilateral IFG correlated significantly with current age (left IFG: r = -.481, p < .05; right IFG: r = -.601, p < .01) and was limited to the AN group. We speculate that decreased IFG volume might lead to deficits in executive functioning or inhibitory control within neural reward systems. Precocious or unbalanced neurological trimming within this particular region might be an important factor for the pathogenesis of AN onset.


Anorexia nervosa (AN) is an eating disorder that generally begins during late childhood or adolescence among females. AN’s core psychopathology is characterized by the relentless pursuit of weight loss, mostly through self-starvation, and a distorted body image [1]. Based on recent epidemiology research, the lifetime prevalence of AN ranges from 0.5% to 2% [2], with a peak age at onset of 13 to 18 years [3]. Additionally, AN has a tendency toward chronification and a mortality rate of 7% [4]. Currently, the exact cause of AN is still unknown; interactions between genetic and biological diathesis, environmental and sociocultural influences, and psychological traits have been assumed [5]. Evidence for the heritability of eating disorders has increased, with AN showing rates between 33% and 84% in certain twin studies [2]. Based on this evidence, neurobiological factors are now being studied in AN; yet the neural mechanisms underlying the cause and persistence of the disorder remain poorly understood [67].

Several studies using brain imaging techniques have identified structural and functional abnormalities in cerebral regions among patients with AN. Previous structural imaging studies have consistently observed reduced gray matter (GM) and white matter (WM) volumes and increased cerebrospinal fluid among AN patients [812]. Other studies have reported an association between brain volume loss and AN severity [10,1314] and persistence of GM reduction [1516]. In recent years, the automated voxel-based morphometry (VBM) technique has been used to measure brain volume differences, which allows for a statistical, voxel-by-voxel assessment of differences across the whole brain. VBM enables the avoidance of operator-dependent manual tracing, a time-consuming technique susceptible to human error. Additionally, VBM allows for statistical estimations between clinical samples and controls on a whole or local brain level [17]. However, while the VBM approach is powerful for examining specific structural regions potentially related to AN pathology, this technique has shown variable results among AN samples. One recent meta-analysis of nine VBM studies revealed that AN patients not only have global reductions in GM, WM, and increased CSF but also show regional decreases in reward and somatosensory regions [17].

Recent functional neuroimaging studies using AN subjects suggested impairment in reward system and/or cognitive control brain areas, which are salient phenotypic characteristics of the disorder. In terms of the reward system, AN patients show increased dopamine receptor functioning during recovery [18], but demonstrate dopamine dysfunction as reflected in dopaminergic hypoactivity within striatal regions when processing pleasant stimuli [4]. This would suggest that one underlying mechanism for AN is related to anhedonia, reflected in impairment of experiencing reward or pleasure. However, this conclusion is not always consistent with other recent studies showing that AN patients show increased reward-related activity in response to images of underweight women [19] and increased activity in other reward-related regions (i.e., insula and orbitofrontal cortex) for food stimuli [2021].

Conversely, reduced prefrontal cortex (PFC) activity has often been observed among AN patients compared to healthy controls (HCs) using functional neuroimaging techniques [2225]. The PFC is a key region for executive functioning, which includes inhibitory control, working memory, and planning. Behavioral studies have revealed that AN patients often have difficulties with tasks requiring inhibitory control, including response inhibition and task switching [26]. Thus, inhibitory control might be a key component influencing the salient characteristics of AN. Functional imbalance between reward and inhibitory control in the cerebral cortex, especially the PFC, might be related to AN susceptibility or pathology. However, regional structural relevance regarding functional abnormalities in AN remain largely unknown [2728]. Moreover, the PFC is one of the last brain regions to fully develop relative to subcortical regions [29]. PFC organization undergoes dynamic changes in terms of synaptic density throughout prepubescence and adolescence [30]. This might be one key reason why peak AN onset tends to be in the teenage years.

The purpose of the present study was to investigate changes in brain morphology using a VBM approach to address mechanisms underlying the emergence of AN during adolescence. We predicted that AN during adolescence reflects a developmental trajectory among brain regions involved in reward and executive functioning. We investigated differences in GMV using an unbiased, whole-brain, voxel-by-voxel approach. Control subjects were used as a comparison group. Several covariates, including age, age at onset and disease duration, were used to control for other factors that could influence brain development. The current study also assessed whether alterations in regional GMV might be correlated with intellectual ability.



Participants included 20 un-medicated girls with AN (mean age, 14.2 years; standard deviation [SD], 1.81 years; age range, 12–17 years), who were referred to our laboratory between 2008 and 2011. All patients satisfied diagnostic criteria for the restrictive subtype of AN based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR #307.1) and had amenorrhea. To exclude other psychiatric conditions, a licensed child and adolescent psychiatrist administered a Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) [31, 32]. Mean Body Mass Index (BMI) for the AN group was 14.4; SD, 2.08.

Matched controls comprised 14 healthy girls (mean age, 14.9 years; SD, 1.59 years; age range, 11–16 years), who were recruited from the community (primarily from school samples). Age, handedness, and full scale intelligence quotient (FSIQ) matched between the AN and control groups. None of the participants (in either group) had any psychiatric psychopathology or head trauma. All participants were right-handed and un-medicated. All scans in both groups were carried out on the same machine over the same time-period. An additional exclusion criterion for both healthy and AN subjects was an FSIQ below 75, as measured by the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV) [33].

The Committee of Life Ethics, Graduate School of Medicine, Kumamoto University, approved the study protocol. The parents of all participants gave written informed consent. The experimental protocol was conducted in accordance with the Declaration of Helsinki.

Brain imaging and analysis

High-resolution, sagittal T1-weighted and coronal FLAIR magnetic resonance (MR) imaging datasets were obtained using a Trio scanner (3T; Siemens Medical Solutions, Siemens AG, Erlangen, Germany). An inversion-prepared three-dimensional multi-planar rapidly acquired gradient echo (MPRAGE) sequence and an eight-element phased-array RF reception coil (Siemens AG) was also employed.

A generalized autocalibrating partially parallel acquisition (GRAPPA) and processing were used to reduce scan time, with a GRAPPA factor of 2. Scan parameters were as follows: echo time, 2.74 ms; repetition time, 2.1 s; inversion time, 1.1 s; flip angle, 12°; three-dimensional matrix, 256 × 256 × 128; field of view, 256 × 256 × 170 mm; bandwidth, 48.6 kHz; scan time, 4 min 56 s. Licensed neuroradiologists conducted assessment of the FLAIR or T1-weighed images, completing their evaluations before the neuroimaging study began.

Voxel-based morphometry (VBM) is a fully automated whole-brain morphometric technique used to detect regional structural differences between groups on a voxel-by-voxel basis [3435]. The VBM was performed using Statistical Parametric Mapping version 8 (SPM8) software (Wellcome Department of Imaging Neuroscience, University College London, London, UK; implemented in MATLAB 9.5 (MathWorks Inc., Natick, MA). Images were segmented coarsely into gray matter, white matter, cerebrospinal fluid, and skull/scalp compartments using tissue probability maps. A standard template was conformed to the space defined by the International Consortium for Brain Mapping [3637], National Institutes of Health P-20 project, and approximates the space described in the Talairach and Tournoux atlas [38]. The transform for this normalization was used to rewrite the original image into the standard space. Volume changes induced by normalization were adjusted via a modulation algorithm. Spatially normalized images were segmented into gray and white matter and then smoothed using a 12-mm full-width half-maximum isotropic Gaussian kernel. Regional GMV differences between groups were analyzed using a general linear model. Potential confounding effects of age and whole segment GMV were modeled, and attributable variances were excluded [39]. The resulting set of voxel values was used to generate a statistical parametric map of t-statistics (SPM{t}), which was transformed to a unit normal distribution (SPM{Z}) for comparison. The statistical threshold was set at p < .05, with correction for multiple comparisons at the cluster level (height threshold of Z > 3.09), given an increased sensitivity within clusters to detect spatially extended signal changes [4041]. Inference testing was based on Gaussian field theory [42]. Potential problems related to non-isotropic smoothness, which can invalidate cluster-level comparisons [34], were corrected by adjusting the cluster size from the resel per voxel image [40, 43].

Statistical analyses

SPM8 was used to identify regions that differed significantly between groups and to assess the association between alterations in GMV in an identified cluster. Clinical values were compared in both groups using two-tailed t-tests. Data are expressed in terms means ± SD. All statistical tests (ANOVA or ANCOVA) were two-sided; p values less than .05 were inferred as statistically significant. Statistical analyses were performed using IBM SPSS 20.0 for Windows (Statistical Package for the Social Sciences; IBM). Exploratory correlation analyses were used to assess whether regional differences in GMV could account for a significant portion of observed variance. Similarly, additional correlation analyses were performed to identify possible associations between GMV and demographic variables within the AN group.


Subject characteristics

Characteristics related to the AN and control participants are summarized in Table 1. The AN and control groups were well matched in terms of age (t = 1.29, p = .21) and FSIQ (t = 1.08, p = .29). AN participants had a lower average BMI; BMI was not assessed in the control group. The mean disease duration was 12.6 months (SD, 1.82; range, 5–69). None of our patients exhibited evidence of focal structural abnormalities on FLAIR or T1-weighed MRI scans.

Table 1. Clinical variables assessed among anorexia and control participants.

Global volume changes

Two-tailed independent t-tests assessing group differences in total GMV (tGMV) revealed significant effects (Table 2). The AN group showed a significant, 10.0% decrease in tGMV as compared to the control group.

Table 2. Volumetric variable comparisons between the anorexia and control groups.

Region-specific GM changes

After correcting for age and tGMV, regional GMV decreases in the bilateral inferior frontal gyrus (IFG) were observed among AN participants (Table 2). An analysis of region-specific GM changes (ANCOVA with age and total GMV as control variables) yielded two clusters in the bilateral IFG (Fig 1; Table 3). The most prominent neural finding was a significant decrease in GMV in the left IFG in patients with AN (Brodmann area 45/46 [BA45/46]; Talairach’s coordinates x = -52, y = 30, z = 24, Z = 4.40, k = 434 voxels; p < .001 for height, FWE corrected to p < .05 for multiple comparisons). The mean decrease in GMV in this area was 19.1% among AN patients. Similarly, significant decreases in GMV in the right IFG were observed in the AN group (BA45/46; Talairach’s coordinates x = 58, y = 30, z = 20, Z = 4.04, cluster size = 225 voxels; p < .001 for height, corrected to p < .05 for multiple comparisons using cluster size). The mean decrease in GMV in this area was 17.6%. No other area showing decreased GMV was found to have a corrected cluster probability value approaching significance.

Table 3. Regional gray matter volume decreases in AN patients compared with the control group.

Fig 1. Voxel based morphometry results.

Locations displaying significant differences between AN and control participants in regional GMV. Significantly lower GM densities among AN participants were revealed in the bilateral IFG (Brodmann area 45/46). Color scale: 0–5 represents t-values.

Correlations between GM changes and clinical variables among AN patients

Correlation analyses within the AN group were conducted to assess the relationship between bilateral IFGs (BA 45/46) and clinical variables (age, FSIQ, BMI at the time of scanning, age at onset, duration of AN, and tGMV). As presented in Table 4, significant correlations were found between regional GMVs in the bilateral IFG and age, BMI, age at onset, and tGMV. To exclude the effects of tGMV and BMI related to GMVs in the IFG and clinical variables, partial correlations were analyzed. After correcting for tGMV and BMI, there was a moderate negative correlation between the GMV of each IFG and age (left: r = -.475, p < .05; right: r = -.601, p < .01) and age at onset (left: r = -.678, p < .01; right: r = -.570, p < .05) (Table 4). Meanwhile, significant correlations between the GMV of each IFG and age were not found in the HC group (left: r = .169, p > .10; right: r = .102, p > .10).

Table 4. Correlations between volumetric and clinical variables within the AN group.


The aim of the present study was to examine global and local structural GM alterations in patients with AN during adolescence using VBM. Results revealed a significant decrease in total GMV among the AN group compared with the control group. Moreover, after controlling for age and tGMV, regional GMV decreases in the bilateral IFG were observed among the AN group. This unexpected finding emerged from a global VBM analytical approach. In addition, the volume decrease in IFG had a negative association with age and disease onset; this association was not found among the control group. These results suggest that volumetric decreases in the IFG might be involved in the impaired cognitive control of impulsivity observed in patients with AN. Furthermore, volumetric decreases in the IFG are likely dependent on age, suggesting that AN symptom progression may have a significant developmental component.

Several studies have indicated individuals suffering AN also exhibit pathological impulsivity; however, this is more common among individuals suffering bulimia nervosa. Askenazy et al. (1998) found that AN subjects show high rates of impulsive behavior (e.g. non-premeditated suicide attempts, self-harm, kleptomania, and alcohol use) and significantly higher impulsivity scores compared to control group subjects [44]. In the current study, we found that the AN group exhibited a reduced bilateral IFG volume, which was significantly involved in several executive functions (including response inhibition and cognitive control). Given previous studies demonstrating that the right IFG is highly involved in the inhibitory control of motor responses [4546], recent reviews suggest that the region is specifically involved in the inhibitory control of inappropriate behaviors [47]. Similarly, a previous VBM study of adult AN subjects has reported regional GMV decreases in the right IFG [48]. Although there is less reported evidence of a role for the left IFG in inhibitory behavioral control, recent brain imaging studies have shown that the left inferior frontal region is involved in impulsivity regulation in healthy adults [49], delayed recruitment of inhibitory control in healthy adolescents [50], and deficits of cognitive control in problem gamers [51]. To the best of our knowledge, the present study is the first to suggest volumetric decreases in either the left or right IFGs in adolescents with AN. Our findings suggest that decreases in these regions influence AN pathogenesis and/or severity.

Previous functional imaging studies have reported reduced PFC activity in adult AN subjects when engaged in a cognitive control task, relative to HCs [2225]. While those studies have shown that AN patients need less inhibitory control to maintain behavioral performance, a recent resting-state fMRI study of AN patients demonstrated functional alterations in the bilateral IFG [52]. Specifically, this study demonstrated functional connectivity from the right IFG to the midcingulum and from the bilateral orbitofrontal gyrus to the right IFG in patients with AN. Additionally, altered functional connectivity from the bilateral insula to the left IFG was observed, indicating a specific connection between the cognitive control system and regions involved in basic motivational processing [52].

The present study also assessed correlations between GM volume changes and clinical variables within the AN group, revealing significant associations between regional GMV in the bilateral IFG and FSIQ, age, age at onset, BMI, and tGMV. Moreover, after controlling for tGMV and BMI, there was a moderate negative correlation between IFG volume and both age and age at onset. These results indicate that particular psychophysiological factor(s) during adolescent development might be closely tied to AN progression and severity. For adult eating disorder patients, most functional imaging studies have reported the IFG as a region demonstrating high neural activity. We were unable to fully clarify specific underlying mechanisms related to GMV and AN emergence in adolescence. However, it is well known that brain maturation during adolescence is characterized by elimination and refinement of synaptic connections in the cerebral cortex, resulting in decreased GM density [53]. Therefore, we speculate that precocious or unbalanced refinement of neuronal connections within the IFG might be an important factor related to pathogenesis of AN during adolescence.

VBM studies provide an unbiased assessment of regional alterations in GMV. However, these studies have a significant number of limitations that must be acknowledged and controlled for as much as possible. Care was taken to make sure that there were no issues with alignment. Subjects in the two groups had an almost identical mean age, and were selected from a narrow age range to minimize any potential developmental differences in template registration. It should be noted that BMI may influence GMV measurements, as it was recently reported that structural brain differences between AN and HC may simply reflect a substantial difference in body weights [54]. Unfortunately, BMI was not assessed in the control group. However, several previous studies have reported an association between BMI and total GMV in AN, suggesting that total GMV likely reflects the patient’s BMI in a reciprocal manner [14, 55]. Moreover, recent studies assessed brain imaging differences between AN and HC groups using total GMV as a confounding factor instead of BMI [56, 57]. Therefore in this study, we likewise used total GMV, as well as age, as confounding factors for VBM analysis. Therefore, the potentially confounding influence of differences in BMI between AN patients and HCs seems to have least been partly accounted for in the present study.

A second notable limitation of the present study was the small sample size of the HC group. This is perhaps why we did not observe any significant association between age and volumetric IFG reduction within the control sample. Although we speculate that this age-related reduction is observed among healthy adolescent females, the reduction slope with aging is much smaller when compared to decreases observed among AN patients.

Finally, we did not obtain an eating disorder inventory for the AN group. Therefore, a possible association between the severity of AN and the regional volume reductions observed in our study could not be investigated. Additional studies that carefully account for multiple clinical assessments will be required to demonstrate such a relationship.

In conclusion, the present study found significant tGMV and regional GMV decreases in the bilateral IFG in adolescent individuals with AN. This GM decrease, which is inferred to be within a region highly relevant to inhibitory control, might explain the presence of impulsive behaviors observed in AN. Further research is necessary to confirm these findings and clarify the causes and consequences of such structural alterations.


We would like to thank Dr. Kei Mizuno for providing technical assistance with MRI data organization and analysis.

Author Contributions

Conceived and designed the experiments: AT. Performed the experiments: HM EY MM AT. Analyzed the data: TXF CY. Contributed reagents/materials/analysis tools: AT. Wrote the paper: TXF CY AT.


  1. 1. American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (4th ed., Text Revision). Washington, DC: Author.
  2. 2. Sigel E (2008) Eating disorders. Adolescent Medicine: State of the Art Reviews, 19, 547–572, xi. pmid:19227391
  3. 3. Weaver L, Liebman R (2011) Assessment of anorexia nervosa in children and adolescents. Current Psychiatry Reports, 13, 93–98. pmid:21221860
  4. 4. Kaye WH, Fudge JL, Paulus M (2009) New insights into symptoms and neurocircuit function of anorexia nervosa. Nature Reviews Neuroscience, 10, 573–584. pmid:19603056
  5. 5. Campbell K, Peebles R (2014) Eating disorders in children and adolescents: state of the art review. Pediatrics, 134, 582–592. pmid:25157017
  6. 6. Gaudio S, Nocchi F, Franchin T, Genovese E, Cannatà V, Longo D, et al. (2011) Gray matter decrease distribution in the early stages of Anorexia Nervosa restrictive type in adolescents. Psychiatry Research, 191, 24–30. pmid:21081268
  7. 7. Kaye WH, Wierenga CE, Bailer UF, Simmons AN, Bischoff-Grethe A (2013) Nothing tastes as good as skinny feels: the neurobiology of anorexia nervosa. Trends in Neurosciences, 36, 110–120. pmid:23333342
  8. 8. Dolan RJ, Mitchell J, Wakeling A (1988) Structural brain changes in patients with anorexia nervosa. Psychological Medicine, 18, 349–353. pmid:3399587
  9. 9. Krieg JC, Pirke KM, Lauer C, Backmund H (1988) Endocrine, metabolic, and cranial computed tomographic findings in AN. Biological Psychiatry, 23, 377–387. pmid:3342267
  10. 10. Golden NH, Ashtari M, Kohn MR, Patel M, Jacobson MS, Fletcher A, et al. (1996) Reversibility of cerebral ventricular enlargement in anorexia nervosa, demonstrated by quantitative magnetic resonance imaging. Journal of Pediatrics, 128, 296–301. pmid:8636835
  11. 11. Kohn MR, Ashtari M, Golden NH, Schebendach J, Patel M, Jacobson MS, et al. (1997) Structural brain changes and malnutrition in anorexia nervosa. Annals of the New York Academy of Sciences, 817, 398–399. pmid:9239217
  12. 12. Frank GK, Bailer UF, Henry S, Wagner A, Kaye WH (2004) Neuroimaging studies in eating disorders. CNS spectrums, 9, 539–548. pmid:15208513
  13. 13. Kohlmeyer K, Lehmkuhl G, Poutska F (1983) Computed tomography of anorexia nervosa. American Journal of Neuroradiology, 4, 437–438. pmid:6410765
  14. 14. Katzman DK, Lambe EK, Mikulis DJ, Ridgley JN, Goldbloom SD, Zipursky RB (1996) Cerebral gray matter and white matter volume deficits in adolescent girls with anorexia nervosa. Journal of Pediatrics, 129, 794–803. pmid:8969719
  15. 15. Katzman DK, Zipursky RB, Lambe EK, Mikulis DJ (1997) A longitudinal magnetic resonance imaging study of brain changes in adolescents with anorexia nervosa. Archives of Pediatrics & Adolescent Medicine, 151, 793–797.
  16. 16. Lambe EK, Katzman DK, Mikulis DJ, Kennedy SH, Zipursky RB (1997) Cerebral gray matter volume deficits after weight recovery from anorexia nervosa. Archives of General Psychiatry, 54, 537–542. pmid:9193194
  17. 17. Titova OE, Hjorth OC, Schiöth HB, Brooks SJ (2013) Anorexia nervosa is linked to reduced brain structure in reward and somatosensory regions: a meta-analysis of VBM studies. BMC Psychiatry, 13, 110. pmid:23570420
  18. 18. Frank GK, Bailer UF, Henry SE, Drevets W, Meltzer CC, Price JC, et al. (2005) Increased dopamine D2 / D3 receptor binding after recovery from anorexia nervosa measured by positron emission tomography and [11c]raclopride. Biological Psychiatry, 58,908–912. pmid:15992780
  19. 19. Fladung AK, Gron G, Grammer K, Herrnberger B, Schilly E, Grasteit S, et al. (2010). A neural signature of anorexia nervosa in the ventral striatal reward system. American Journal of Psychiatry, 167, 206–212. pmid:19833790
  20. 20. Frank GK, Reynolds JR, Shott ME, Jappe L, Yang TT, Tregellas JR, et al. (2012) Anorexia nervosa and obesity are associated with opposite brain reward response. Neuropsychopharmacology, 37, 2031–2046. pmid:22549118
  21. 21. Uher R, Murphy T, Brammer MJ, Dalgleish T, Phillips ML, Ng VW, et al. (2004) Medial prefrontal cortex activity associated with symptom provocation in eating disorders. American Journal of Psychiatry, 161, 1238–1246. pmid:15229057
  22. 22. Lock J, Garrett A, Beenhakker J, Reiss AL (2011) Aberrant brain activation during a response inhibition task in adolescent eating disorder subtypes. American Journal of Psychiatry, 168, 55–64. pmid:21123315
  23. 23. Nagamitsu S, Yamashita F, Araki Y, Iizuka C, Ozono S, Komastu H, et al. (2010) Characteristic prefrontal blood volume patterns when imaging body type, high-calorie food, and mother-child attachment in childhood anorexia nervosa: A near infrared spectroscopy study. Brain & Development, 32, 162–167.
  24. 24. Nagamitsu S, Araki Y, Ioji T, Yamashita F, Ozono S, Kouno M, et al. (2011) Prefrontal brain function in children with anorexia nervosa: a near-infrared spectroscopy study. Brain & Development, 33, 35–44.
  25. 25. Oberndorfer TA, Kaye WH, Simmons AN, Strigo IA, Matthews SC (2011) Demand-specific alteration of medial prefrontal cortex response during an inhibition task in recovered anorexic women. International Journal of Eating Disorders, 44, 1–8. pmid:20127942
  26. 26. Galimberti E, Martoni RM, Cavallini MC, Erzegovesi S, Bellodi L (2012) Motor inhibition and cognitive flexibility in eating disorder subtypes. Progress in Neuro-psychopharmacology & Biological Psychiatry, 36, 307–312.
  27. 27. Castro-Fornieles J, Bargalló N, Lázaro L, Andrés S, Falcon C, Plana MT, et al. (2009) A cross-sectional and follow-up voxel-based morphometric MRI study in adolescent anorexia nervosa. Journal of Psychiatric Research, 43, 331–340. pmid:18486147
  28. 28. Chui HT, Christensen BK, Zipursky RB, Richards BA, Hanratty MK, Kabani NJ, et al. (2008) Cognitive function and brain structure in females with a history of adolescent-onset anorexia nervosa. Pediatrics, 122, e426–37. pmid:18676530
  29. 29. Somerville LH, Casey BJ (2010) Developmental neurobiology of cognitive control and motivational systems. Current Opinion in Neurobiology, 20, 236–241. pmid:20167473
  30. 30. Chowdhury U, Gordon I, Lask B, Watkins B, Watt H, Christie D (2003) Early-onset anorexia nervosa: is there evidence of limbic system imbalance? International Journal of Eating Disorders, 33, 388–396. pmid:12658668
  31. 31. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. (1998) The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59, 22–33. pmid:9881538
  32. 32. Sheehan DV, Sheehan KH, Shytle RD, Janavs J, Bannon Y, Rogers JE, et al. (2010) Reliability and validity of the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). The Journal of Clinical Psychiatry, 71, 313–336. pmid:20331933
  33. 33. Wechsler D (2003) The Wechsler intelligence scale for children—fourth edition. London: Pearson Assessment.
  34. 34. Good CD, Johnsrude I, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001a) Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. NeuroImage, 14, 685–700. pmid:11506541
  35. 35. Good CD, Johnsrude I, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001b) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14, 21–36.
  36. 36. Ashburner J, Friston KJ (2000) Voxel-based morphometry-the methods. NeuroImage, 11, 805–821. pmid:10860804
  37. 37. Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage, 26, 839–851. pmid:15955494
  38. 38. Talairach J, Tournoux P (1988) Co-planar Stereotaxic Atlas of the Human Brain: 3-dimensional Proportional System: an Approach to Medical Cerebral Imaging. Stuttgart: Thieme. pmid:8978477
  39. 39. Tomoda A, Navalta CP, Polcari A, Sadato N, Teicher MH (2009) Childhood sexual abuse is associated with reduced gray matter volume in visual cortex of young women. Biological Psychiatry, 66, 642–648. pmid:19560122
  40. 40. Hayasaka S, Phan KL, Liberzon I, Worsley KJ, Nichols TE (2004) Nonstationary cluster-size inference with random field and permutation methods. NeuroImage, 22, 676–687. pmid:15193596
  41. 41. Moorhead TW, Job DE, Spencer MD, Whalley HC, Johnstone EC, Lawrie SM (2005) Empirical comparison of maximal voxel and non-isotropic adjusted cluster extent results in a voxel-based morphometry study of comorbid learning disability with schizophrenia. NeuroImage, 28, 544–552. pmid:16085427
  42. 42. Friston KJ, Holmes A, Poline JB, Price CJ, Frith CD (1996) Detecting activations in PET and fMRI: levels of inference and power. NeuroImage, 4, 223–235. pmid:9345513
  43. 43. Worsley KJ, Andermann M, Koulis T, MacDonald D, Evans AC (1999) Detecting changes in nonisotropic images. Human Brain Mapping, 8, 98–101. pmid:10524599
  44. 44. Askenazy F, Candito M, Caci H, Myquel M, Chambon P, Darcourt G, et al. (1998) Whole blood serotonin content, tryptophan concentrations, and impulsivity in anorexia nervosa. Biological Psychiatry, 43, 188–195. pmid:9494700
  45. 45. Asahi S, Okamoto Y, Okada G, Yamawaki S, Yokota N (2004) Negative correlation between right prefrontal activity during response inhibition and impulsiveness: a fMRI study. European Archives of Psychiatry and Clinical Neuroscience, 254, 245–251. pmid:15309395
  46. 46. Duann JR, Ide JS, Luo X, Li CS (2009) Functional connectivity delineates distinct roles of the inferior frontal cortex and presupplementary motor area in stop signal inhibition. Journal of Neuroscience, 29, 10171–10179. pmid:19675251
  47. 47. Aron AR, Robbins TW, Poldrack RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends in Cognitive Sciences, 18, 177–185. pmid:24440116
  48. 48. Joos A Klöppel S, Hartmann A, Glauche V, Tüscher O, Perlov E, et al. (2010) Voxel-based morphometry in eating disorders: correlation of psychopathology with grey matter volume. Psychiatry Research, 182, 146–151. pmid:20400273
  49. 49. Schilling C, Kühn S, Romanowski A, Schubert F, Kathmann N, Gallinat J. Cortical thickness correlates with impulsiveness in healthy adults. Neuroimage, 59, 824–830. pmid:21827861
  50. 50. Vara AS, Pang EW, Vidal J, Anagnostou E, Taylor MJ (2014) Neural mechanisms of inhibitory control continue to mature in adolescence. Developmental Cognitive Neuroscience, 10, 129–139. pmid:25212682
  51. 51. Luijten M, Meerkerk GJ, Franken IH, van de Wetering BJ, Schoenmakers TM (2015) An fMRI study of cognitive control in problem gamers. Psychiatry Research: Neuroimaging, 231, 262–268. pmid:25670645
  52. 52. Kullmann S, Giel KE, Teufel M, Thiel A, Zipfel S, Preissl H (2014) Aberrant network integrity of the inferior frontal cortex in women with anorexia nervosa. Neuroimage Clinical, 4, 615–622. pmid:24936412
  53. 53. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW (2003) Mapping cortical change across the human life span. Nature Neurosciences, 6, 309–315. pmid:12548289
  54. 54. King JA, Geisler D, Ritschel F, Boehm I, Seidel M, Roschinski B, et al. (2015) Global cortical thinning in acute anorexia nervosa normalizes following long-term weight restoration. Biological Psychiatry, 77, 624–632. pmid:25433902
  55. 55. Treasure J, Claudino AM, Zucker N (2010) Eating disorders. Lancet, 375, 583–593. pmid:19931176
  56. 56. Brooks SJ, Barker GJ, O'Daly OG, Brammer M, Williams SC, Benedict C, et al. (2011) Restraint of appetite and reduced regional brain volumes in anorexia nervosa: a voxel-based morphometric study. BMC Psychiatry, 11, 179. pmid:22093442
  57. 57. Friederich HC, Walther S, Bendszus M, Biller A, Thomann P, Zeigermann S, et al. (2012) Grey matter abnormalities within cortico-limbic-striatal circuits in acute and weight-restored anorexia nervosa patients. Neuroimage, 59, 1106–1113. pmid:21967727