Assessment of Eating Behavior Disturbance and Associated Neural Networks in Frontotemporal Dementia | Dementia and Cognitive Impairment | JAMA Neurology | JAMA Network
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Figure 1.  Eating Behavior in Patients With Frontotemporal Dementia: Results of the Ad Libitum Breakfast Test Meal and Dessert Experiment
Eating Behavior in Patients With Frontotemporal Dementia: Results of the Ad Libitum Breakfast Test Meal and Dessert Experiment

Panel A, Box plot showing total caloric intake for the ad libitum breakfast test meal. Ends of boxes represent the first and third quartiles. Lines in the boxes represent median values. Whiskers represent the minimum and maximum values. Panels B and C, For the dessert experiment, patients’ mean liking scores for desserts A (26% sucrose), B (39% sucrose), and C (60% sucrose) and patients’ mean perceived sweetness scores for desserts A, B, and C are shown.

aFor the ad libitum breakfast test meal, mean intake in the behavioral variant frontotemporal dementia (bvFTD) group was greater than for all other groups (P < .001). For the dessert A, liking ratings of the patients with bvFTD were less than the ratings of all other groups (P < .01).

bFor the dessert C, liking ratings of the patients with bvFTD and semantic dementia were greater than the ratings of all other groups (P < .001).

Figure 2.  Voxel-Based Morphometry Analyses for Total Intake and Sucrose Preference and Visual Representation of Proposed Networks That Control Total Intake and Sucrose Preference in Frontotemporal Dementia (FTD)
Voxel-Based Morphometry Analyses for Total Intake and Sucrose Preference and Visual Representation of Proposed Networks That Control Total Intake and Sucrose Preference in Frontotemporal Dementia (FTD)

Voxel-based morphometry analyses revealed brain regions in which gray matter intensity correlates significantly with total caloric intake in patients with behavioral variant FTD (bvFTD) (Montreal Neurological Institute [MNI] coordinates: x = 18, y = 40): higher total caloric intake on the ad libitum breakfast test meal correlated with gray matter intensity decrease in a number of brain regions likely involving a network connecting the anterior cingulate, which connects to the thalamus and is involved in taste via connections to the insula and reward via connections to the basal ganglia. The thalamus also likely connects to the hypothalamus, with neuroendocrine modulation of reward, and the lingual gyrus and visual cortex, for visual input to reward processing. The cerebellum also likely modulates eating behavior through autonomic input and cerebellar hypothalamic connections. In patients with semantic dementia (MNI coordinates: x = 18, y = 40), the orbitofrontal cortex is likely involved in decision making regarding preferences that may explain the rigid eating behavior in this group. This then aligns with a similar network in bvFTD involving reward and taste (left thalamus, insula, amygdala, bilateral nucleus accumbens). Additional contributions of lingual gyrus and cerebellar inputs are likely in semantic dementia eating behavior. Significant associations were found between sucrose preference (liking of most sweet dessert [60% sucrose]) in patients with bvFTD and semantic dementia combined (MNI coordinates: x = 20, y = −8). The network for sucrose involves the orbitofrontal cortex connecting right insula-striatal reward structures and nucleus accumbens. Again the cerebellum and lingual gyrus are involved in this network. Colored voxels show regions that were significant in the covariate analyses with P < .05 corrected for false discovery rate. Clusters are overlaid on the MNI standard brain. Age is included as a covariate in the analyses. L indicates left.

Table 1.  Demographic and Clinical Characteristics of Patient Groups and Healthy Controlsa
Demographic and Clinical Characteristics of Patient Groups and Healthy Controlsa
Table 2.  Total Intake and Percentage Macronutrient Intake on the Ad Libitum Breakfast Test Meal and Total Intake of Each Dessert in Patient Groups and Controlsa
Total Intake and Percentage Macronutrient Intake on the Ad Libitum Breakfast Test Meal and Total Intake of Each Dessert in Patient Groups and Controlsa
Table 3.  Voxel-Based Morphometry Resultsa
Voxel-Based Morphometry Resultsa
Original Investigation
March 2016

Assessment of Eating Behavior Disturbance and Associated Neural Networks in Frontotemporal Dementia

Author Affiliations
  • 1Neuroscience Research Australia, Sydney
  • 2University of New South Wales, Sydney, Australia
  • 3Australian Research Council Centre of Excellence in Cognition and Its Disorders, University of New South Wales, Sydney, Australia
  • 4Brain and Mind Centre, Sydney Medical School, University of Sydney, Sydney, Australia
  • 5University of Cambridge Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome Trust–Medical Research Council Institute of Metabolic Science, Cambridge, United Kingdom
JAMA Neurol. 2016;73(3):282-290. doi:10.1001/jamaneurol.2015.4478
Abstract

Importance  Abnormal eating behaviors are common in patients with frontotemporal dementia (FTD), yet their exact prevalence, severity, and underlying biological mechanisms are not understood.

Objective  To define the severity of abnormal eating behavior and sucrose preference and their neural correlates in patients with behavioral variant FTD (bvFTD) and semantic dementia.

Design, Setting, and Participants  Forty-nine patients with dementia (19 with bvFTD, 15 with semantic dementia, and 15 with Alzheimer disease) were recruited, and their eating behavior was compared with that of 25 healthy controls. The study was conducted from November 1, 2013, through May 31, 2015, and data analyzed from June 1 to August 31, 2015.

Main Outcomes and Measures  Patients participated in an ad libitum breakfast test meal, and their total caloric intake and food preferences were measured. Changes in eating behavior were also measured using the Appetite and Eating Habits Questionnaire (APEHQ) and the Cambridge Behavioral Inventory (CBI). Sucrose preference was tested by measuring liking ratings of 3 desserts of varying sucrose content (A: 26%, B: 39%, C: 60%). Voxel-based morphometry analysis of whole-brain 3-T high-resolution brain magnetic resonance imaging was used to determine the gray matter density changes across groups and their relations to eating behaviors.

Results  Mean (SD) ages of patients in all 4 groups ranged from 62 (8.3) to 66 (8.4) years. At the ad libitum breakfast test meal, all patients with bvFTD had increased total caloric intake (mean, 1344 calories) compared with the Alzheimer disease (mean, 710 calories), semantic dementia (mean, 573 calories), and control groups (mean, 603 calories) (P < .001). Patients with bvFTD and semantic dementia had a strong sucrose preference compared with the other groups. Increased caloric intake correlated with atrophy in discrete neural networks that differed between patients with bvFTD and semantic dementia but included the cingulate cortices, thalami, and cerebellum in patients with bvFTD, with the addition of the orbitofrontal cortices and nucleus accumbens in patients with semantic dementia. A distributed network of neural correlates was associated with sucrose preference in patients with FTD.

Conclusions and Relevance  Marked hyperphagia is restricted to bvFTD, present in all patients with this diagnosis, and supports its diagnostic value. Differing neural networks control eating behavior in patients with bvFTD and semantic dementia and are likely responsible for the differences seen, with a similar network controlling sucrose preference. These networks share structures that control cognitive-reward, autonomic, neuroendocrine, and visual modulation of eating behavior. Delineating the neural networks involved in mediating these changes in eating behavior may enable treatment of these features in patients with complex medical needs and aid in our understanding of structures that control eating behavior in patients with FTD and healthy individuals.

Introduction

Marked changes in eating behavior are one of the criteria for the diagnosis of behavioral variant frontotemporal dementia (bvFTD).1 Eating behavior changes are also increasingly recognized in patients with semantic dementia, with rigid eating behavior (eating same foods repeatedly) and changes in food preference reported.2,3 However, despite their central role in the diagnosis of bvFTD, eating changes have been measured mostly on the basis of caregiver questionnaires.2-6 This approach is unlikely to provide a complete account of the extent and severity of changes because of subjective interpretation of a patient’s behavior and a tendency for patients to hide these behaviors.

Neuroimaging studies6,7 have suggested that overeating in patients with bvFTD is associated with atrophy in the right ventral, insula, striatum, and orbitofrontal cortices. A retrospective data analysis6 related eating behavior to changes in the right ventral putamen and pallidum, key regions in reward-seeking circuits of the brain. Caregivers of patients with FTD often report sweet food–seeking behaviors, a behavior that has been associated with gray matter loss that involves bilateral orbitofrontal cortices and the right anterior insula.5

Ecologically valid assessments are crucial in understanding the characteristics of eating behaviors in these debilitating disorders. Research in obesity has used real meals to examine eating behavior and changes in food preferences, including sucrose preference.8-10 The present study aimed to (1) quantify eating behavior in patients with bvFTD and semantic dementia using ecologically valid methods, notably an ad libitum breakfast and sucrose preference approach, and (2) identify the neural correlates of these eating behaviors using voxel-based morphometry analyses of high-resolution structural brain magnetic resonance imaging (MRI).

Box Section Ref ID

Key Points

  • Question: What are the neural networks that control eating behavior in behavioral variant frontotemporal dementia (bvFTD) and semantic dementia?

  • Findings: In this study of 49 patients with dementia and 25 healthy controls, using methods from obesity research, all patients with bvFTD had hyperphagia, whereas those with semantic dementia had rigid eating behavior, and both groups had a strong sucrose preference. Differing neural networks are associated with eating behaviors in patients with bvFTD and semantic dementia, with similar networks associated with sucrose preference.

  • Meaning: Delineating the neural networks that control eating behavior will aid in understanding this complex behavioral change and possible treatment options for patients with FTD.

Methods
Patients

Forty-nine patients with dementia (19 with bvFTD, 15 with semantic dementia, and 15 with Alzheimer disease [AD]) were recruited from Neuroscience Research Australia. All patients met current clinical diagnostic criteria for probable bvFTD, semantic dementia, or AD.11-14 Disease severity was established using the Frontal Rating Scale (FRS).15 In addition, 25 healthy controls were recruited from a panel of healthy study volunteers in Sydney, Australia (19 individuals), and Cambridge, United Kingdom (6 individuals). Healthy controls scored above 88 of 100 on the Addenbrooke’s Cognitive Examination–Revised (ACE-R).16 The patient and control groups were matched specifically for age, sex, and body mass index (BMI) (calculated as the weight in kilograms divided by height in meters squared) to remove their potential effects on eating behavior. In addition to the ad libitum breakfast test meal (see procedure below), changes in eating behavior were measured using caregiver-based questionnaires: the Appetite and Eating Habits Questionnaire (APEHQ)2,3 and the Cambridge Behavioral Inventory (CBI).17 Height and weight were measured (shoes removed) and BMI derived. The study was conducted from November 1, 2013, through May 31, 2015.

Study 1: Ad Libitum Breakfast Test Meal

Participants presented after a 10-hour fast. The night before they were supplied with a meal that represented 35% of their calculated predicted total daily intake. After taking a fasting blood sample, participants were offered a buffet-style ad libitum breakfast meal and left alone for 30 minutes to eat their breakfast. This buffet comprised a selection of foods, including cereals, bread, and sweet and savory foods (total of 5424 calories). After completion, each item was weighed to calculate the total amount consumed in calories and total macronutrients (percentage of fat, protein, carbohydrate, and sugar) consumed.

Study 2: Sucrose Preference

After 4 hours of fasting, patients participated in a dessert tasting. Three options of Eton mess dessert (a traditional English dessert consisting of a mixture of strawberries, pieces of meringue, and cream) varying in sugar content were offered (A: 26%, B: 39%, C: 60%). Participants were given 10-g tasting pots of each dessert and asked to rank on a visual analog scale from 0 to 10 how much they liked each dessert and how sweet it was. They were then left in the room with 1 large bowl of each dessert for 15 minutes and asked to consume the dessert until they were comfortably full. The total amount of each dessert consumed in grams was documented.

Brain MRI Acquisition and Analyses

All participants underwent whole-brain 3-T high-resolution T1 imaging on the day of the eating experiments. The MRI data were analyzed with FSL-VBM, voxel-based morphometry analysis software, using the FSL-VBM toolbox from the FMRIB software package (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fslvbm/index.html).18-21 A voxelwise general linear model was used to investigate gray matter intensity differences through permutation-based nonparametric testing22 with 5000 permutations per contrast. Differences in cortical gray matter intensities between patients (bvFTD, semantic dementia, and AD groups) and controls were assessed using t tests (eTable in Supplement). Clusters were extracted using the threshold free cluster enhancement method and corrected for familywise error at P < .05.

Next, correlations between total caloric intake and regions of gray matter atrophy were investigated in each FTD group separately (ie, semantic dementia and bvFTD groups, given the differences in behavior). For sucrose preference ratings, the bvFTD and semantic dementia groups were combined, because of similar behavior, to examine correlations with liking ratings for the most sweet dessert C (60% sucrose) and regions of gray matter intensity. For additional statistical power, a covariate-only statistical model with a [−1] t-contrast was used, providing an index of association between decreasing gray matter volume and increased intake and sucrose preference ratings. Age was included as a nuisance variable in the covariate analyses. An unbiased whole-brain approach was used across all atrophy and covariate voxel-based morphology analyses. Anatomical locations of significant results were overlaid on the Montreal Neurological Institute standard brain, with coordinates of maximum change provided in Montreal Neurological Institute stereotaxic space. For all covariate analyses, clusters were extracted using a voxelwise approach and corrected for false discovery rate at P < .05. Anatomical labels were determined with reference to the Harvard-Oxford probabilistic cortical atlas.

Standard Approvals and Consents

This study was approved by the South Eastern Sydney Area Health District and the University of New South Wales human ethics committees. Written informed consent was obtained.

Statistical Analyses

Data were analyzed using SPSS statistical software, version 21.0 (SPSS Inc). Kolmogorov-Smirnov tests were run to determine suitability of variables for parametric analyses. Analyses of variance, followed by Tukey post hoc tests, were used to determine group differences for the demographic and clinical (age, ACE-R score, disease duration, educational level) and eating (APEHQ, total CBI, CBI eating, BMI) (P ≤ .05 was regarded as significant) variables. Because of nonnormal distribution, group differences in total caloric intake, nutrient intake, and sweet likeness and perceived sweetness scores on the dessert experiment and cognitive measures of executive function and disinhibition were analyzed using nonparametric Kruskal-Wallis tests followed by post hoc Mann-Whitney tests corrected for multiple comparisons (P ≤ .01 was regarded as significant). Associations of total intake on the ad libitum breakfast test meal, BMI, and dessert sweet preference with eating behavior surveys, cognitive scores, and disease severity were further explored using Spearman rank correlations corrected for multiple comparisons (P ≤ .01 was regarded as significant).

Results

Demographic variables did not differ across groups (Table 1) (P > .18 for all). Group differences were observed on measures of cognition (ACE-R) and measures of executive function and disinhibition and disease severity (Table 1). The bvFTD group was more functionally impaired relative to the AD (FRS; P = .009) and semantic dementia groups (P < .001). The bvFTD group had more severe eating disturbance based on caregiver surveys (P < .005 for all). Groups were matched for BMI (Table 1).

Study 1: Ad Libitum Breakfast Test Meal

Group differences were present for total caloric intake as measured by the ad libitum breakfast test meal (H3 = 40.5, P < .001) (Figure 1 and Table 2). Total caloric intake in the bvFTD group notably had no overlap with the AD and control groups. The patients with semantic dementia had rigid eating behavior, often refusing to eat the food on offer (patient who scored 0 on intake) or only eating small amounts. No group differences were present for macronutrient intake (Table 2) (P > .24 for all), apart from total protein intake (H3 = 18.6, P < .001), with the control group consuming a higher percentage of protein compared with the bvFTD group (U = 61, P < .001).

Study 2: Sucrose Preference

Five patients with semantic dementia refused to partake in the experiment, stating that they did not like the dessert. The mean liking ratings for each of the desserts (A: 26% sucrose, B: 39% sucrose, C: 60% sucrose) and the perceived sweetness of each dessert are shown in Figure 1. No group differences were observed for the perceived sweetness of each dessert: all groups ranked A as the least sweet, B as middle sweetness, and C as most sweet (A: H3 = 5.3, P = .15; B: H3 = 4.2, P = .24; C: H3 = 1.8, P = .62). In contrast, group differences emerged regarding the liking rating of each dessert: a group effect was present for the least sweet dessert (A) (H3 = 16.7, P = .001), with the bvFTD group liking this dessert less than the AD (U = 38.5, P = .002), semantic dementia (U = 37.0, P = .01), and control (U = 67.5, P < .001) groups. No group differences were evident on liking of the middle sweetness dessert (B) (H3 = 3.1, P = .38). For the most sweet dessert (C), a group effect was again observed (H3 = 34.7, P < .001), with the bvFTD group liking it more than the AD (U = 11.0, P < .001) and control (U = 27.0, P < .001) groups. The liking rating of dessert C did not differ between the bvFTD and semantic dementia groups. The semantic dementia group liked dessert C more than the AD (U = 11.0, P = .001) and control (U = 18.0, P < .001) groups. Total intake (Table 2) of each dessert significantly varied across groups (A: H3 = 21.7, P < .001); B: H3 = 28.4, P < .001; C: H3 = 30.1, P < .001), with the bvFTD group consuming more of each dessert than the other groups (P < .005 for all).

Correlations Between Eating Behavior and Clinical and Functional Measures

With all patient groups combined, total caloric intake on the ad libitum breakfast test meal was associated with total scores on caregiver ratings of eating changes on the APEHQ (rs = 0.496, P < .001), CBI eating (rs = 0.502, P < .001), overall ratings of behavioral changes (CBI total: rs = 0.598, P < .001), and level of functional impairment (FRS: rs = −0.599, P < .001). Body mass index also correlated with CBI eating (rs = 0.336, P = .005), CBI total (rs = 0.316, P = .009), and FRS scores (rs = −0.363, P = .01). Body mass index did not correlate with total caloric intake on the ad libitum breakfast test meal (rs = 0.148, P = .27).

Liking of the least sweet dessert (ie, dessert A) was negatively correlated with total APEHQ score (rs = −0.540, P < .001) and total calories consumed during the breakfast study (rs = −0.414, P = .001). In other words, a lower preference for the least sweet dessert correlated with higher abnormal eating behavior. Increased liking of the most sweet dessert (ie, dessert C) correlated with total CBI eating (rs = 0.594, P < .001), CBI total (rs = 0.642, P < .001), FRS (rs = −0.464, P = .003), and total calories consumed for the breakfast study (rs = 0.539, P < .001). No correlations were present between total caloric intake and measures of executive function and disinhibition.

Voxel-Based Morphometry Analyses

In the bvFTD group, high caloric intake on the breakfast study correlated with decrease in gray matter density in the cingulate cortices, inferior temporal structures extending posteriorly, the thalami, right hippocampus, right cerebellum, occipital cortex, and lingual gyrus (Figure 2 and Table 3). Similar regions were associated with caloric intake in the semantic dementia group, generally more so in the left than the right hemisphere, with the addition of the bilateral orbitofrontal cortices and nucleus accumbens (Figure 2 and Table 3). Combining the bvFTD and semantic dementia groups revealed that preference for the most sweet dessert (60% sucrose) was associated with frontal, right insula-striatal reward structures, and nucleus accumbens, occipital, and cerebellum gray matter intensity decrease (Figure 2 and Table 3).

Discussion

This study applied novel, ecologically valid methods to quantify food intake and sucrose preference in bvFTD and semantic dementia, which share clinical and pathologic features. This innovative approach uncovered an abnormally elevated total caloric intake and hyperphagia exclusively in patients with bvFTD, supporting its diagnostic value for this disease, with no overlap in total intake between the bvFTD group and AD and control groups. Its specificity further supports its use as a marker to differentiate bvFTD from other dementia syndromes. The semantic dementia group overall did not exhibit increased total caloric intake although a number of the patients with semantic dementia had rigid eating behavior. These findings confirm that the rigid eating behavior of patients with semantic dementia influences their food preferences and eating practices.3,23

On testing of sucrose preference, patients with bvFTD and semantic dementia had a strong liking for the most sweet dessert compared with the AD and control groups, with the bvFTD group also having a decreased liking for the least sweet dessert. These findings support the strong sweet preference reported in patients with bvFTD4,5 but also indicate that this preference extends to patients with semantic dementia. Despite these preferences, the groups did not differ with regard to the perceived sweetness of the desserts, indicating that increased sweet preference in patients with FTD is not simply the result of an inability to perceive sweetness5 but rather likely involves changes in preference for sucrose as a nutrient.

Turning to the neural correlates, previous studies based on caregiver surveys have suggested that eating changes in bvFTD are associated with atrophy in predominantly right-sided anterior and subcortical structures,5,7 attributable to dysregulation of reward pathways.6 Our findings uncovered complex mechanisms underlying changes in eating behavior in patients with FTD, suggesting changes in distributed functional neural networks24 that involve reward, visual, autonomic, and neuroendocrine processes, with subtle differences between bvFTD and semantic dementia (Figure 2).

Increased caloric intake in patients with bvFTD on the ad libitum breakfast test meal correlated with atrophy of bilateral anterior and posterior cingulate gyri, the thalamus, bilateral lateral occipital cortex, lingual gyri, and the right cerebellum. Unlike previous studies,5,7,24 we did not find involvement of the orbitofrontal cortex in the bvFTD group, suggesting that eating behavior in this group is not simply related to a failure of inhibitory control.25-27 This position is further supported by an absence of relation between total caloric intake and measures of disinhibition on cognitive testing. Instead, we found involvement of the anterior cingulate gyrus, which participates in decision making, response selection,28 anticipation of reward, task reinforcement,26,29 and controlling visceromotor, endocrine, and skeletomotor outputs,30 potentially via integration of cognitive with autonomic information.31 In healthy individuals, activity of the cingulate cortex has been associated with increased BMI, suggesting a role for this structure in regulating eating.32 These cognitive aspects likely interact with reward processes via connections between the cingulate cortex and thalamic nuclei,30,33 which are implicated in the integration of taste via connections with the gustatory cortex in the insula,34 and reward, acting as a relay center between the basal ganglia and frontal structures.35

In addition, the thalamus is connected with the hypothalamus,36 which plays a key role in appetite and satiety control via a system of neuroendocrine peptides. Hypothalamic atrophy has been found in vivo and pathologically in patients with bvFTD,37 and elevated levels of agouti-related peptide, a key hypothalamic peptide that encourages hyperphagic behavior, have been found in patients with bvFTD, suggesting that hypothalamic changes may modulate the control of eating behavior in patients with bvFTD through interactions with cortical structures.38 In the current study, we also found an association between cerebellar integrity and total caloric intake in patients with bvFTD. In healthy individuals, the cerebellum is involved in feeding via autonomic and visceral control,38,39 and the cerebellum has been found to be involved in bvFTD.40 Finally, contribution of visual information to eating behavior in patients with bvFTD, potentially via feedback into reward pathways, is likely because total caloric intake was associated with volume loss in the lateral occipital and lingual cortices, suggesting a visual association role, which has been found in other diagnoses with abnormal eating behavior, such as Prader-Willi syndrome.41

Different mechanisms underlying changes in eating behavior in patients with semantic dementia were uncovered. In this group, the brain regions that correlated with total caloric intake differed from those in the patients with bvFTD, notably with the involvement of the bilateral orbitofrontal cortices, left hippocampus, left thalamus, left amygdala, left insula, bilateral nucleus accumbens, right temporal fusiform cortex, right temporal occipital fusiform cortex, right parahippocampal gyrus, bilateral lingual gyri, and right cerebellum. Many of these structures are core to the semantic deficits seen in semantic dementia,42,43 suggesting a contribution of semantic networks to eating control, possibly secondary to the loss of knowledge concerning foods. Indeed, complex interactions appear to come into play, with the left thalamus and nucleus accumbens implying involvement of reward processing and taste and the insular cortex, gustatory cortex,34 and amygdala implying involvement of emotional and learning responses to food. Given the lack of association of total intake with the orbitofrontal cortices in patients with bvFTD, it seems plausible that this association in patients with semantic dementia reflects evaluative choice and decision making in keeping with their rigid eating behavior.

The neural correlates of sucrose preference in patients with bvFTD and semantic dementia revealed a brain network different from that involved in overall food intake. Increased liking of the most sweet dessert C (60% sucrose) correlated with volume loss in bilateral orbitofrontal cortices and predominantly right-sided insula-striatal structures, including the nucleus accumbens, amygdala extending into the temporal occipital cortex, lingual gyrus, and cerebellum. A functional imaging study44 of sucrose preference in healthy individuals has implicated a network that involves the transmission of sensory information from tongue taste receptors via cranial nerves to the nucleus tractus solitarius and to the thalamic ventroposterior medial nucleus and then to the primary gustatory cortex involving the frontal operculum and anterior insula. Animal models have also suggested connections between the hypothalamus and reward areas.45 The network found in patients with bvFTD and semantic dementia for sucrose preference hence parallels that known to be implicated in sucrose preference in healthy individuals.

Given the marked increase in food intake in the bvFTD group, one would expect their BMI to be abnormal. However, BMI did not correlate with total intake on the ad libitum breakfast test meal, strongly suggesting that other variables influence BMI, including increased energy expenditure, which may be related to involvement of the anterior cingulate and insula, both of which modulate the autonomic nervous system.46 Increased energy expenditure (hypermetabolism) is a well-documented phenomenon in amyotrophic lateral sclerosis,47 which has a strong clinical and pathologic overlap with FTD.48 Future research is required as to whether this phenomenon also exists in bvFTD. Future work should also explore the contribution of impaired semantic knowledge to the eating behavioral changes seen in patients with semantic dementia.

Conclusions

Strong sucrose preference is a marker of FTD syndromes, whereas hyperphagia is present in all patients with bvFTD, and semantic dementia is characterized by rigid eating behavior, with dissociated neural networks responsible for these changes. An understanding of the networks that control this eating behavior offers opportunities for targeted treatments that can modify eating behavior, metabolic abnormalities, and disease progression49 and provides insights into structures that control eating behavior in healthy individuals.

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Article Information

Accepted for Publication: November 18, 2015.

Corresponding Author: Rebekah M. Ahmed, MBBS, Neuroscience Research Australia, Barker Street, Randwick, New South Wales 2031, Australia (r.ahmed@neura.edu.au).

Published Online: January 25, 2016. doi:10.1001/jamaneurol.2015.4478.

Author Contributions: Drs Ahmed and Hodges had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Ahmed, Farooqi, Hodges.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ahmed, Irish, Henning, Dermody, Bartley, Kiernan, Farooqi, Hodges.

Critical revision of the manuscript for important intellectual content: Ahmed, Henning, Bartley, Kiernan, Piguet, Farooqi, Hodges.

Statistical analysis: Ahmed, Irish, Dermody.

Obtained funding: Kiernan, Piguet.

Administrative, technical, or material support: Henning, Bartley, Farooqi.

Study supervision: Kiernan, Piguet, Farooqi, Hodges.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by grant 1037746 from the National Health and Medical Research Council of Australia (NHMRC) (Drs Kiernan and Hodges), grant CE110001021 from the Australian Research Council Centre of Excellence in Cognition and its Disorders Memory Node (Drs Piguet and Hodges), and project grant 1003139 from the NHMRC to Forefront, a collaborative research group dedicated to the study of frontotemporal dementia and motor neuron disease. Dr Ahmed is a Royal Australasian College of Physicians PhD scholar and MND Australia PhD scholar. Dr Irish is supported by grant DE130100463 from the ARC Discovery Early Career Researcher Award Fellowship program. Dr Piguet is supported by grant 1022684 from the NHMRC Career Development Research Fellowship program. Dr Farooqi is supported by the Wellcome Trust, Medical Research Council, European Research Council, National Institute for Health Research Cambridge Biomedical Research Centre, and The Bernard Wolfe Endowment.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Additional Contributions: We are grateful to the research participants involved with the ForeFront research studies. Heidi Cartwright, BSc, assisted with the figures. No compensation was provided.

References
1.
Piguet  O, Hornberger  M, Shelley  BP, Kipps  CM, Hodges  JR.  Sensitivity of current criteria for the diagnosis of behavioral variant frontotemporal dementia.  Neurology. 2009;72(8):732-737.PubMedGoogle ScholarCrossref
2.
Ahmed  RM, Irish  M, Kam  J,  et al.  Quantifying the eating abnormalities in frontotemporal dementia.  JAMA Neurol. 2014;71(12):1540-1546.PubMedGoogle ScholarCrossref
3.
Ikeda  M, Brown  J, Holland  AJ, Fukuhara  R, Hodges  JR.  Changes in appetite, food preference, and eating habits in frontotemporal dementia and Alzheimer’s disease.  J Neurol Neurosurg Psychiatry. 2002;73(4):371-376.PubMedGoogle ScholarCrossref
4.
Miller  BL, Darby  AL, Swartz  JR, Yener  GG, Mena  I.  Dietary changes, compulsions and sexual behavior in frontotemporal degeneration.  Dementia. 1995;6(4):195-199.PubMedGoogle Scholar
5.
Whitwell  JL, Sampson  EL, Loy  CT,  et al.  VBM signatures of abnormal eating behaviours in frontotemporal lobar degeneration.  Neuroimage. 2007;35(1):207-213.PubMedGoogle ScholarCrossref
6.
Perry  DC, Sturm  VE, Seeley  WW, Miller  BL, Kramer  JH, Rosen  HJ.  Anatomical correlates of reward-seeking behaviours in behavioural variant frontotemporal dementia.  Brain. 2014;137(Pt 6):1621-1626.PubMedGoogle ScholarCrossref
7.
Woolley  JD, Gorno-Tempini  ML, Seeley  WW,  et al.  Binge eating is associated with right orbitofrontal-insular-striatal atrophy in frontotemporal dementia.  Neurology. 2007;69(14):1424-1433.PubMedGoogle ScholarCrossref
8.
Zipf  WB, Berntson  GG.  Characteristics of abnormal food-intake patterns in children with Prader-Willi syndrome and study of effects of naloxone.  Am J Clin Nutr. 1987;46(2):277-281.PubMedGoogle Scholar
9.
Lindgren  AC, Barkeling  B, Hägg  A, Ritzén  EM, Marcus  C, Rössner  S.  Eating behavior in Prader-Willi syndrome, normal weight, and obese control groups.  J Pediatr. 2000;137(1):50-55.PubMedGoogle ScholarCrossref
10.
Blundell  J, de Graaf  C, Hulshof  T,  et al.  Appetite control: methodological aspects of the evaluation of foods.  Obes Rev. 2010;11(3):251-270.PubMedGoogle ScholarCrossref
11.
Gorno-Tempini  ML, Hillis  AE, Weintraub  S,  et al.  Classification of primary progressive aphasia and its variants.  Neurology. 2011;76(11):1006-1014.PubMedGoogle ScholarCrossref
12.
Rascovsky  K, Hodges  JR, Knopman  D,  et al.  Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.  Brain. 2011;134(Pt 9):2456-2477.PubMedGoogle ScholarCrossref
13.
McKhann  GM, Albert  MS, Grossman  M, Miller  B, Dickson  D, Trojanowski  JQ; Work Group on Frontotemporal Dementia and Pick’s Disease.  Clinical and pathological diagnosis of frontotemporal dementia.  Arch Neurol. 2001;58(11):1803-1809.PubMedGoogle ScholarCrossref
14.
McKhann  GM, Knopman  DS, Chertkow  H,  et al.  The diagnosis of dementia due to Alzheimer’s disease.  Alzheimers Dement. 2011;7(3):263-269.PubMedGoogle ScholarCrossref
15.
Mioshi  E, Hsieh  S, Savage  S, Hornberger  M, Hodges  JR.  Clinical staging and disease progression in frontotemporal dementia.  Neurology. 2010;74(20):1591-1597.PubMedGoogle ScholarCrossref
16.
Mioshi  E, Dawson  K, Mitchell  J, Arnold  R, Hodges  JR.  The Addenbrooke’s Cognitive Examination Revised (ACE-R).  Int J Geriatr Psychiatry. 2006;21(11):1078-1085.PubMedGoogle ScholarCrossref
17.
Bozeat  S, Gregory  CA, Ralph  MA, Hodges  JR.  Which neuropsychiatric and behavioural features distinguish frontal and temporal variants of frontotemporal dementia from Alzheimer’s disease?  J Neurol Neurosurg Psychiatry. 2000;69(2):178-186.PubMedGoogle ScholarCrossref
18.
Irish  M, Addis  DR, Hodges  JR, Piguet  O.  Considering the role of semantic memory in episodic future thinking.  Brain. 2012;135(Pt 7):2178-2191.PubMedGoogle ScholarCrossref
19.
Kumfor  F, Irish  M, Hodges  JR, Piguet  O.  The orbitofrontal cortex is involved in emotional enhancement of memory.  Brain. 2013;136(Pt 10):2992-3003.PubMedGoogle ScholarCrossref
20.
Ashburner  J, Friston  KJ.  Voxel-based morphometry.  Neuroimage. 2000;11(6, pt 1):805-821.PubMedGoogle ScholarCrossref
21.
Smith  SM, Jenkinson  M, Woolrich  MW,  et al.  Advances in functional and structural MR image analysis and implementation as FSL.  Neuroimage. 2004;23(suppl 1):S208-S219.PubMedGoogle ScholarCrossref
22.
Nichols  TE, Holmes  AP.  Nonparametric permutation tests for functional neuroimaging.  Hum Brain Mapp. 2002;15(1):1-25.PubMedGoogle ScholarCrossref
23.
Snowden  JS, Bathgate  D, Varma  A, Blackshaw  A, Gibbons  ZC, Neary  D.  Distinct behavioural profiles in frontotemporal dementia and semantic dementia.  J Neurol Neurosurg Psychiatry. 2001;70(3):323-332.PubMedGoogle ScholarCrossref
24.
Irish  M, Piguet  O, Hodges  JR.  Self-projection and the default network in frontotemporal dementia.  Nat Rev Neurol. 2011;8(3):152-161.PubMedGoogle ScholarCrossref
25.
Rangel  A, Hare  T.  Neural computations associated with goal-directed choice.  Curr Opin Neurobiol. 2010;20(2):262-270.PubMedGoogle ScholarCrossref
26.
Rushworth  MF, Behrens  TE, Rudebeck  PH, Walton  ME.  Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behaviour.  Trends Cogn Sci. 2007;11(4):168-176.PubMedGoogle ScholarCrossref
27.
Hornberger  M, Geng  J, Hodges  JR.  Convergent grey and white matter evidence of orbitofrontal cortex changes related to disinhibition in behavioural variant frontotemporal dementia.  Brain. 2011;134(Pt 9):2502-2512.PubMedGoogle ScholarCrossref
28.
Devinsky  O, Morrell  MJ, Vogt  BA.  Contributions of anterior cingulate cortex to behaviour.  Brain. 1995;118(Pt 1):279-306.PubMedGoogle ScholarCrossref
29.
Amiez  C, Joseph  JP, Procyk  E.  Reward encoding in the monkey anterior cingulate cortex.  Cereb Cortex. 2006;16(7):1040-1055.PubMedGoogle ScholarCrossref
30.
Vogt  BA, Finch  DM, Olson  CR.  Functional heterogeneity in cingulate cortex.  Cereb Cortex. 1992;2(6):435-443.PubMedGoogle Scholar
31.
Critchley  HD, Mathias  CJ, Dolan  RJ.  Neural activity in the human brain relating to uncertainty and arousal during anticipation.  Neuron. 2001;29(2):537-545.PubMedGoogle ScholarCrossref
32.
Volkow  ND, Wang  GJ, Telang  F,  et al.  Inverse association between BMI and prefrontal metabolic activity in healthy adults.  Obesity (Silver Spring). 2009;17(1):60-65.PubMedGoogle ScholarCrossref
33.
Behrens  TE, Johansen-Berg  H, Woolrich  MW,  et al.  Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging.  Nat Neurosci. 2003;6(7):750-757.PubMedGoogle ScholarCrossref
34.
Samuelsen  CL, Gardner  MP, Fontanini  A.  Thalamic contribution to cortical processing of taste and expectation.  J Neurosci. 2013;33(5):1815-1827.PubMedGoogle ScholarCrossref
35.
Haber  SN, Knutson  B.  The reward circuit: linking primate anatomy and human imaging.  Neuropsychopharmacology. 2010;35(1):4-26.PubMedGoogle ScholarCrossref
36.
Kullmann  S, Heni  M, Linder  K,  et al.  Resting-state functional connectivity of the human hypothalamus.  Hum Brain Mapp. 2014;35(12):6088-6096.PubMedGoogle ScholarCrossref
37.
Piguet  O, Petersén  A, Yin Ka Lam  B,  et al.  Eating and hypothalamus changes in behavioral-variant frontotemporal dementia.  Ann Neurol. 2011;69(2):312-319.PubMedGoogle ScholarCrossref
38.
Ahmed  RM, Latheef  S, Bartley  L,  et al.  Eating behavior in frontotemporal dementia.  Neurology. 2015;85(15):1310-1317.PubMedGoogle ScholarCrossref
39.
Zhu  JN, Wang  JJ.  The cerebellum in feeding control: possible function and mechanism.  Cell Mol Neurobiol. 2008;28(4):469-478.PubMedGoogle ScholarCrossref
40.
Tan  RH, Devenney  E, Dobson-Stone  C,  et al.  Cerebellar integrity in the amyotrophic lateral sclerosis-frontotemporal dementia continuum.  PLoS One. 2014;9(8):e105632.PubMedGoogle ScholarCrossref
41.
Ogura  K, Fujii  T, Abe  N,  et al.  Regional cerebral blood flow and abnormal eating behavior in Prader-Willi syndrome.  Brain Dev. 2013;35(5):427-434.PubMedGoogle ScholarCrossref
42.
Rosen  HJ, Gorno-Tempini  ML, Goldman  WP,  et al.  Patterns of brain atrophy in frontotemporal dementia and semantic dementia.  Neurology. 2002;58(2):198-208.PubMedGoogle ScholarCrossref
43.
Irish  M, Hodges  JR, Piguet  O.  Right anterior temporal lobe dysfunction underlies theory of mind impairments in semantic dementia.  Brain. 2014;137(Pt 4):1241-1253.PubMedGoogle ScholarCrossref
44.
Ogawa  H.  Gustatory cortex of primates: anatomy and physiology.  Neurosci Res. 1994;20(1):1-13.PubMedGoogle ScholarCrossref
45.
Nieh  EH, Matthews  GA, Allsop  SA,  et al.  Decoding neural circuits that control compulsive sucrose seeking.  Cell. 2015;160(3):528-541.PubMedGoogle ScholarCrossref
46.
Jones  SE.  Imaging for autonomic dysfunction.  Cleve Clin J Med. 2011;78(suppl 1):S69-S74.PubMedGoogle ScholarCrossref
47.
Desport  JC, Torny  F, Lacoste  M, Preux  PM, Couratier  P.  Hypermetabolism in ALS.  Neurodegener Dis. 2005;2(3-4):202-207.PubMedGoogle ScholarCrossref
48.
Lomen-Hoerth  C, Anderson  T, Miller  B.  The overlap of amyotrophic lateral sclerosis and frontotemporal dementia.  Neurology. 2002;59(7):1077-1079.PubMedGoogle ScholarCrossref
49.
Ahmed  RM, MacMillan  M, Bartley  L,  et al.  Systemic metabolism in frontotemporal dementia.  Neurology. 2014;83(20):1812-1818.PubMedGoogle ScholarCrossref
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